February 18, 2025 • 36 mins
Meet @SundasKhalid: High school dropout, immigrant, and now a powerhouse in data at Google! She shares pivotal tips for breaking into data, invaluable financial literacy insights, and how she champions salary negotiation by helping others secure higher pay.
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⌚ TIMESTAMPS
00:00 - Introduction
01:05 - From high school dropout, immigrant child, to analytics lead at Google!
15:24 - Number 1 piece of advice
19:36 - AI in the workplace
24:04 - Financial literacy and salary negotiation
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Avery (00:00):
If you're breaking into data right now, you've probably seen one of
(00:03):
Sundus Khalid's videos with over 250,000 subscribers on both YouTube and
Instagram and absolutely killer content.
She's near impossible to miss.
She's worked as a data analyst, adata engineer, and a data scientist.
At both Amazon and Google.
But in today's episode, you'regoing to hear Sundance's
story in a whole new light.
(00:24):
You see, you know Sundance as the rockstar at Google and Amazon that she is.
But she's actually an immigranthigh school dropout who didn't even
speak English until later in life.
She didn't go to an Ivy Leagueschool, and she even started
her career later than most.
She is living proof that it'snever too late to break into
data, no matter your background.
So coming up, you'll hear Sundasnumber one data skill that you
(00:45):
need to learn no matter what.
Sundas Khalid (00:46):
I would have to pick a coding language, and it's gonna be
Avery (00:49):
s t.
If she likes being a data analyst, adata scientist, or a data engineer more.
I
Sundas Khalid (00:53):
don't want to pick.
I would say like d k ismy favorite for building.
And
Avery (00:58):
her crazy financial journey and what you can take from it.
Nobody
Sundas Khalid (01:01):
keeps.
That much money in their bank account.
Like people invest with
Avery (01:05):
that.
Let's get into the episode.
Sundance.
I'm so excited to have you onbecause you have such a unique story.
You're a high school dropout,immigrant child, and now you're an
analytics lead at freaking Google.
So how on earth did you get here?
Sundas Khalid (01:19):
First of all, thank you so much, Avery, for
having me on your podcast.
Um, and thanks for a great intro.
It's a long story, but I think likeyou summarize it really, really well.
I am a high school dropout and I'man immigrant and I was six years gap
between my high school and my university.
So when I look back now to like 10, 15years ago, I can't believe that I am here.
(01:40):
So it's been a long journey, a littlebumpy, but I am really grateful for all
the support that I've had throughout my.
career and throughoutmy education journey.
So a TLDR is that I went to Universityof Washington, went to business
school, and in the business school,I actually learned about data
analytics, databases, SQL and whatnot.
And that's where my love and mypassion started for the data field.
(02:04):
Then I just kept building on top of it.
I didn't have enough time to graduatewith a CS or a data science degree.
So I ended up building on my own,like continuing learning on my own.
So I'm a self taught dataengineer, data scientist.
Data analysts, likewhatever you want to call.
So it's been a long winded journey, butI'm so happy to be here and I'm happy
to answer and deep dive into any ofthese topics, uh, you'll let me know.
Avery (02:26):
Well, I'm super excited because I think there's a lot of people who
are watching this, who are like you,who might be immigrants to the U S who,
you know, maybe started school a littlebit later or later in their career.
And they're like, man, I don'tknow how the heck I'm going
to break into data analytics.
I think you're living proofthat like you can start late.
You can start disadvantaged.
Like you didn't even start speakingEnglish till later in life.
(02:46):
And you can still end up on thetop, which I think is really cool.
And also you didn't go tolike a brand name university.
You didn't study computer science.
You didn't study math.
You kind of just studied business.
Uh, what's been like the, the biggestthing for you in your career journey?
That's allowed you to, toget to where you're at.
Sundas Khalid (03:03):
Um, so I think like a couple of things that helped me
really daily, uh, in my career.
One is.
Knowing what I want to do andwhen I don't know what I want
to do, like I still kept going.
So when I started in my career, like myfirst internship, what am I was at Amazon?
Um, I was lucky enoughto get that internship.
It was really coincidentalbecause I learned about that
(03:24):
internship at a networking eventwhile I was going to school.
Prior to that, I was gettingrejected from all the internships.
From my experience, like I havealways been open to trying new things.
And Amazon is something that Ididn't want to try initially,
but I just jumped into it.
One of my best friends at that time,um, he actually worked at Amazon and
(03:44):
he was like, no, you have two kids.
There's no way you're evergoing to survive at Amazon.
I was like, no, I have to try itfor myself and I have to go for it.
I ended up going for it.
And that was ended up being oneof the best career decisions that
I've made because one, Amazontook a lot of chances on me.
Like it let me try outnew things, for example.
My first job was a data engineer,uh, which I like passed the technical
(04:05):
interview screen, but there was still alot that I needed to learn on the job.
So my teammates, my seniormembers on the team.
basically taught me a lot duringmy first job as a data engineer.
Uh, secondly, like one of the advicethat I got from my mentor early on
is, um, I couldn't figure out, I, Iwould always meet people and they were,
they were always like so passionateabout specific topics, specific area.
(04:27):
And I wasn't really likepassionate, passionate about it.
I think I was doing dataengineering at that time.
So I asked my mentor,like, what should I do?
I know I'm not like reallypassionate about something.
In particular, like I like dataengineering, but I don't know
if I want to do that long term.
So his advice to me was sometimesyou find what you're passionate
about and sometimes you don't.
And if you don't know what you'repassionate about, you still keep going
(04:48):
and eventually you'll figure it out.
So that's exactly what I ended up doing.
I did data engineering and Ifound a data scientist role.
And that, for me, like, this is, Iknew, like, that's the exact next
thing that I want to do and I pivoted.
Having the right mentors by my side,having the aptitude to like pivot and
learn new things has been like really,really, really helpful in my career.
(05:09):
And lastly, I, I, I would, I want tosay like luck definitely plays a role.
You being at the right place, righttime definitely puts has some,
there is like some luck involved.
Like it would be unfair if anybody comesto you and say like, it's all hard work.
It's not all hard work.
It's hard work you putting in the work,but also like you have to be at the,
sometimes you have to be at the rightplace, right time for things to happen.
(05:31):
I like to say,
Avery (05:32):
yeah, I like to say the, the harder you work, the luckier
you get a lot of the time.
Like, um, if we go back to, youknow, landing your first day at a
job, you're just a business student.
You've taken a few like it classesin, in your college career.
But at the end of the day,you're like a business major.
Like you said, with two kids,how the heck are you going
to start interning at Amazon?
Um, and you went to that networkingevent and I think that's kudos to you
because a lot of people wouldn't havegone to that networking event because
(05:55):
one, it's like just another thing to do.
Two, those networking events, a lotof the times they're very awkward
and you have to like go up and likepresent yourself to people and you're
like, hi, I'm Sundance and like, youshould hire me and stuff like that.
And so yes, like luck had a big part.
Like they had to be interestedin you at that networking event.
Um, but just like the fact thatlike you showed up, uh, I think is.
That's a lot of people don't.
(06:15):
And that's, that's the hard thing isit's uncomfortable to show up sometimes.
And then the other thing I want to say,uh, about you, Sundance, that I think
has really stuck out to me, uh, we'vegotten to meet, uh, in person for a
couple of days, uh, a year ago, and thenwe've also just gotten interact online is
like, you're a very clear communicator.
Um, like you're very good at likeknowing what you want to say and making
it very easy for the person you'retalking with to understand like, okay,
(06:38):
this is what's on this means thisis like what she's doing and this
is what I should do because of it.
I think that's played like ahuge role in your career as well.
Would you agree?
Sundas Khalid (06:46):
Um, I think that definitely I would agree with that.
And I have to give credit to Amazonbecause, um, Amazon teaches you a way
to like communicate in writing andin talking, like they're very direct.
When I left Amazon and I went toGoogle and I was like asking people
who were previously at Amazon and nowwork at Google, I was like, can you
give me advice, like, uh, tell me whatI need to do differently and their,
(07:08):
uh, their advice to me was that like.
Be a little less, um, I don't wantto say indirect, but like soften,
soften up the language a little bit.
So like when you say that,like I'm not surprised at all.
Like I can be very direct, yeah.
Not as direct as I would like to be,but I can be like very direct and crisp
and clear in terms of like what I want.
And I think that has helped meoutside of work, like in content
(07:30):
creation and like being on YouTubeand, uh, teaching people things.
So it's been helpful.
Avery (07:35):
I agree.
Yeah.
Your YouTube audience, your,your Instagram audience, I
think, uh, would agree as well.
Now, like you said, you started offkind of in this data analyst role.
And then you kind of pivoted todata engineering and then you kind
of pivoted to data scientists.
And so you've actually worked inlike the big three data professions
at both Amazon and Google.
So I'm actually curious, uh, which ofthese positions did you enjoy the most?
Sundas Khalid (07:56):
Um, okay.
So I wanted to say Youknow, it's a tough question.
I left data engineering, solike there has to be a reason.
I would say like, they're all myfavorite for different reasons.
I don't want to pick.
So I would say like data engineeringis my favorite for building.
Like you get to build things.
And this is like one of the things thatI miss about being a data engineer.
Like I don't build things.
(08:17):
I don't build like data pipelinesor platforms that other people use.
And I can, at the end of theyear, I can be like, Oh my God.
These are the number of people that usemy product or the pipeline that I use.
I think like data analysthas some aspect of it.
But like, I definitely miss that fromthe data engineering point of view.
What I don't miss is the on call.
So that's definitely another topic.
Uh, the data scientist world is amazing.
(08:40):
It's just so, so huge in the ambiguity.
I kind of like to have lovehate kind of like a relationship
with like the ambiguity.
But I really love that.
I can actually take an ambiguousproblem and solve it in data science.
Uh, when I was working at Amazonas a data scientist, one of my,
the ideas that I focused on wasA B testing and experimentation.
(09:00):
And the coolest thing about A Btesting and experimentation is that
it would be, like you would run, oneof, some of the tests that we would
run would be very small difference.
For example, you would changethe font color from red to blue.
And you will see like a huge shift incustomer behavior, uh, the purchases,
the orders, and so like things likethat, that I had previously not
(09:21):
thought about, like data sciencerole made me like think about that.
So I really like that aspectof it quite a bit, quite a bit.
In terms of the data scientistjob family, it's humongous.
Like you can be more on the machinelearning side, more on like the
product data scientist side, Iwould say like my favorite one is
definitely product data scientist side,because you get to mix both product.
So you kind of like a data scientisttimes a product manager in one role.
(09:45):
So you're able to like, think ofmore creative ideas and solutions.
As a product manager, but thensolve them, um, as a data scientist.
So I guess like I did pick my favorite.
Avery (09:56):
There you go.
It's data scientists.
You just had to talk it out, I guess.
Yeah.
Um, that's very cool.
I like that.
You talked about like, okay, yeah.
Data engineering is building data.
Scientist is like more experimentingand trying to figure out how we solve.
Real world problems with math.
And then data analyst is somewhere,um, in, in between now, obviously in
those different roles, you've probablybeen using different tech stacks, but
(10:19):
there's definitely some overlap as well.
I'm going to make you choose one again.
If you had to choose one tool you've usedthe most in your career, what tool is it?
Sundas Khalid (10:26):
Okay.
I would have to pick a codinglanguage and it's going to be SQL.
And I don't think it's a surpriseto anybody listening to this
SQL is regardless if you're adata engineer, you're a data
scientist or you're a data analyst.
You have to learn SQL and you have to.
Not even know it, the basics.
You actually have to know that vast levelif you really want to grow in these roles.
In terms of the tools, I would say likeeach role uses different set of tools
(10:51):
and they don't have anything in common.
So like, I'll stick withthe coding language.
Avery (10:55):
I like it.
I think, yeah, maybe that's not asurprise that, uh, SQL, it's like
the most in demand data skill in,and honestly, all three job families.
It seems like, you know, I thinkPython gets close for, for data
scientists, but It's, it's really SQL.
Okay.
So SQL is the tool you've used the most.
Do you, do you, do you have a tool thatyou like to use more than, than SQL?
Sundas Khalid (11:16):
You mean like a coding language or just like coding
Avery (11:19):
language or like Tableau or Looker, or I don't know, like, is
there like some tool you really enjoy?
Sundas Khalid (11:25):
I think the tool that I really, really enjoy is Google Collabs,
um, notebooks, uh, because they arelike so, uh, dynamic, like you can like
code in R, it's like similar to likeJupyter Notebook, but I guess like I
never really, really got the hang ofJupyter Notebooks, I've always been
like a Google Collab person, so I reallylove using Google Collab as like part
of my job, and what I love about it islike you can write any language, like
(11:48):
you can have one notebook and write somany different languages, to produce
the results and you can share that codewith just literally a link with somebody
else that who's going to like take overyour work or like scale it and apply it.
Avery (12:00):
That's huge in, in the workplace, because like, like you said, like
sometimes maybe you're the data scientistand you're writing the code, but you're
not necessarily the person who's going toput it to scale, or maybe you just need to
share it with your manager or some otherproduct owner or something like that.
Uh, but it's also big forthose of you who are listening.
Who haven't landed a data job yet, becauseif you ever do any projects in Python,
if you do it like in Jupyter notebook,you're not going to be able to share it
(12:22):
very easily and like doing it in Googlecollab allows you to like have a link
that you can send to a recruiter orhiring manager and it just makes like
your life easier in terms of sharingthe work that you've actually done.
You've been at Google for five years now.
Um, and, uh, for those of you thathave listened to send us on her
YouTube channel, um, you've, you'vemaybe heard some of her stories.
Um, I highly suggest checking it out.
(12:43):
We'll have a link in theshow notes down below.
One of the cool things that I thinkthat you've done, and we'll get
into negotiation here in a bit.
Um, but you, you know, you wereat Amazon, you actually used.
Like multiple teams at Amazon, not reallyon purpose, but to kind of compete for
you that allowed you to kind of geta little bit more advantageous roles.
And then you interviewed inthe past at like Microsoft
and got offers from Microsoft.
And that allowed you to, youknow, get some, some better
(13:05):
opportunities at places like Google.
Um, but you've been atGoogle for five years now.
Um, can you just tell us like whatyour role is and what do you feel like?
Um, like What you do on a day to day basisand what you feel like you've learned.
Sundas Khalid (13:18):
First of all, like I'm surprised that you watched all those
videos because like some of those,some of that information I know lives,
like the Microsoft offer lives in ourvideos somewhere deep, so I'm grateful
that you watched that, uh, definitelyanother story, like how I use my
Microsoft offer to like get more frommy employer, Amazon at that time, but
in like my current role, um, at Googleis primarily focused on Google search.
(13:41):
Uh, so like when you searchon Google, like you'll see.
Some ads.
So like I work in Google searchads and then there's another tab
that you will see like shopping.
So like the ads that yousee in search and shopping.
So that's the part of my, uh, that's partof my team and that's what I support.
So my work is primarily likefocused on like doing advanced
analytics, like experimentation.
Uh, deep insights and kind of likefiguring out what works and what doesn't.
(14:05):
Um, so it's like a, I would say like,it's a, it's a hybrid of data scientists,
product data scientists, and advanceddata analytics all merged into one.
My typical day to day depends onthe project that I'm working on.
So for example, uh, right now thatthe project that I'm working on that I
told you, like before this call, like.
It's a large scale project, andwe've been working on it for many,
(14:27):
many years, and it's currentlyin the implementation stage.
And while we're implementing, thingsthat could go wrong are going wrong.
So, my current project is figuringout, uh, there is a small traffic
that we launched, and I'm doingan investigation to understand,
like, what exactly is happening.
So, like, doing deep dives thereto, like, root cause the problem.
That's my current focus.
(14:48):
Last month, if you ask me what myday looked like, my last month,
my day was, um, my days wasfocused on my, on experimenting.
So we were running a lotof like sequential testing.
So I was doing a lot of like experimentanalysis, trying to understand how
different arms of the experimentshave performed and what decisions
we need to take and whatnot.
Avery (15:07):
Very cool.
That's, that sounds very cool.
It's so, it's, it's soneat to like hear that.
I like it.
Oh yeah, there is data scientistsworking on this product that I literally
use every single day, you know, andthey're improving the product based
off of what I do with the product.
So I think, uh, that's,that's really cool.
And like for the, for the years thatyou've been there, like, what do you
feel like you've taken away as likeyour number one, like piece of advice?
(15:28):
Like, for instance, ifyou were to go back.
To Sundance five years ago on day oneof starting Google, you actually have a
video, I think, where you did like theday one of Google or something like that.
Uh, if you were to go back andtalk to that Sundance, what
advice would you give her?
And what would you tell her that,that maybe you, you wouldn't
have realized or thought back?
Sundas Khalid (15:43):
So let's go back a bit in terms of like, when I was at Amazon.
So Amazon was my first job andI spent about six, seven years.
If you like count my internshiptime as well, my internship was
eight months long, which is likenot a typical internship time.
time.
So I always wanted to experience industryoutside because Amazon is all I knew.
So when I started looking for jobs, likeI had a few companies in mind that I was
(16:05):
interested in, and Google was one of them.
And let me just say that if I hadn'tjoined Google, or if I hadn't left Amazon,
I wouldn't know like what it's like, youknow, Experiencing different work cultures
and figuring out what I actually like.
I think one of the biggest learning forme personally is learning about like what
type of culture, work culture and workenvironment I want to be part of, uh,
(16:27):
what I need to look for in my next job.
So one of the big thingsthat I immediately learned at
Google or like noticed is theculture and how nice people.
Uh, for example, like I, my Nuglerorientation was in New York.
Um, and I was meeting some of my teammatesthere that I've never met before.
And they were like, where are you based?
(16:47):
I'm like, I'm, I'm in Seattle.
And their response was like, love that.
Love that.
I'm like in my head, I'm like,why are they saying love that?
It's, uh, I've never even met them.
And this is the firsttime I'm meeting them.
Maybe just, they're justtrying to say that to me.
And then.
Weeks past, months past, like thiswas like a normal people behavior.
And eventually it kind of likerubbed onto me as well, where
I picked up that language.
(17:08):
Um, so I would say like thebiggest learning for me has been
like just seeing how people firstculture actually looks like.
And I'll talk, I'll definitely talkabout like how Google has been an
inspiration or has like helped melearn, become financially literate.
Because if I hadn't joinedGoogle, I don't think I would.
I don't want to say ever, but like, Idon't think the chances of me becoming
(17:30):
a financially literate person wouldhave happened if I hadn't joined Google.
So the number one thing definitelystands out is like the culture and
people like, uh, Google has some ofthe nicest people that I've ever met.
And what I like to tell myfriends is like a different world
inside that everybody's just.
Um, really nice to talk to.
It's
Avery (17:49):
it's
Sundas Khalid (17:50):
pleasant.
It's always pleasant.
Just so, I
Avery (17:52):
mean, I think they give those vibes off like, uh, like the
campus seems fun and like playful.
I've seen some of likevideos and pictures there.
And, uh, I mean, like even likethe logo feels a little bit, maybe.
More playful than other companies.
And it is, I think what you said is reallyimportant that like, you need to go out
there and try different companies, um,and maybe even different industries.
Because, uh, what I found in mycareer is I started my, my data
(18:15):
career at a really small biotechstartup that had like 15 employees.
I love them.
Shout out to vapor sense, butlike, you should have seen my desk.
Like it was, it was kind of like abox basically, like in a closet and
uh, like I didn't have nice equipment.
And so when I got an offer to go toExxon mobile, uh, at this huge campus
down in Texas, like this awesome sitstand desk, I was like, Hey, I need
(18:35):
to try that and see what it was like.
And then I got there and I was like.
Crap.
I hate working for a 70, 000 or not70, 000, 70, 000 person company,
uh, in manufacturing and I wouldnever, I would have never known that.
And I'm glad I still did it because Iwould have always been like, well, what
if I like working for a big company thatgives me nice perks, but I actually,
like when I was there, I realized, crap,I want to go back to like the rag tag
team, you know, of like a small company.
(18:57):
And then I started my own company.
Now I'm a company of one and I like that.
Right.
So, um, I think it'sreally cool that like.
At the end of the day, like we'reall, we're at work for, you know,
40 hours a week, most of us, right?
Something like that.
Maybe more, maybe less.
Like we want to be doing something weactually enjoy with people we enjoy
in an environment that we enjoy.
And obviously the money is important,but like if, if you paid me a bajillion
(19:19):
dollars, okay, maybe not a bajillion,but if you paid me a lot of money
to do something I didn't enjoy.
A million!
Okay, if you paid me a milliondollars, but I hate my life,
I don't know if I would do it.
If you paid me a billion, I'm probablyin, but a billion, I don't know.
Sundas Khalid (19:31):
Listen, you get that million, you work for
a year, and then you retire.
So.
Avery (19:34):
Perfect.
There you go.
So obviously one thing thatpeople are really interested
in is like this new wave of AI.
Do you have any tips on like for peopleof how they could be using AI at work?
Sundas Khalid (19:45):
Yeah, um, you know what?
That's a great question because AI islike the new hot topic and literally
anyone, everyone is talking about it.
So if somebody in this world who doesn'tknow what ChatGPT, Gemini or any of
the generative AI tools are, I don'tknow like who you are, please identify
yourself because Literally, everybodyknows it and have at least tried once.
(20:07):
Um, in terms of like using it atwork, I think it's becoming, uh, more
and more popular in the workspace.
Uh, so some of the things thatI have personally done and
use AI for is like coding.
So let's say if I'm writing a SQL codeor a Python code, and I can either,
there's like, um, There's an AI built inthat can help me like finish the code.
(20:28):
I think GitHub AI, what isGitHub's version called?
Avery (20:31):
Copilot is it?
Copilot,
Sundas Khalid (20:32):
yeah, literally basically Copilot and all of these other tools
that like helps you finish coding.
So like coding is definitelyone of the use cases.
So if you are a coder, definitelytake a look at, look into that.
One of the things that I'm really,really proud of is, like, I wrote
a document in less than 30 minutes.
It's a two page document using Gemini,which turned out to be really good.
(20:52):
I did not use the exact copy ofthe Gemini, just for the reference.
Um, I basically got an outline, gotsome, some sections to fill, and then
I turned it into my own language.
Sometimes when you stare at a blackpiece of paper, it's just difficult
to start, so having Gemini builtin, I'm able to kind of like, have
it start, and then I like, I, Ibasically jump in and like, take over.
(21:15):
Then email writing and email summarizing,like sometimes when you have like, long
You can literally use email that is builtinto like Gmail and other email tools to
like summarize the large thread and helpyou understand what exactly it is saying.
So it's like a great time saver.
The two, the last two that I want tomention is like summarizing Google Slides.
Sometimes I get access to likethese large decks that I really
(21:36):
do not want to go through.
So I will just ask Gemini tolike summarize these for me.
And then my last one, my favoriteone so far has been Notebook LM.
Um, I don't know if you havetried Notebook LM, but it's
literally, it's just, Just mindblowing what it's capable of.
You can basically actually did a YouTubevideo on this where I did a walkthrough.
I'm writing my next newsletter is goingto be about notebook LM as well, but
(21:57):
basically you plug in your documents.
You can even linkarticles, YouTube videos.
Um, and you can ask it to like,uh, create summaries, uh, for you.
It's basically like your own tinyrag system that you have built using
NotebookLM that you can like askquestions that are like specific to
the documents that you have imported.
It can also create a podcast for you.
I mean, I can talk aboutit for a very long time.
(22:19):
I love NotebookLM, like one of theprojects that I mentioned earlier,
I'm actually using NotebookLM to likescale all the work to global teams.
Because notebook LM can literally, Ican import like the dozens of documents
that I have from last two years, um,and like build it one repository.
And instead of like somebody who is likeonboarding on this project, instead of
(22:39):
like reading through every document,they can just like ask questions to
notebook LM and like get an answer,which I think is really, really cool.
Okay, I'll tell you one thing.
I don't need to use a tool to figureout if you wrote something with chat
GPT or Gemini, like I can, I can readyour script for like 10 seconds, and
I'll know like you wrote something.
So recently, we're going off topic, butrecently, like, I was interviewing for
(22:59):
my personal assistant position and therewere like 500 applicants and after reading
those 500 applications, like, I kind offigured who wrote with ChatGPT, who wrote
with Gemini and like, what are they doing?
So, use it at your own risk.
But it's a great starting point, butit's not, it shouldn't be your end point.
So I won't be watching videosthat are just had deputy
(23:19):
scripts because I can tell,
Avery (23:20):
I like what you said earlier.
It's like a warm start.
You're not starting froma blank, blank slate.
Um, I know I've been hiring a lot recentlyand there's been multiple candidates,
I would say like close to 10 to 20%.
That forgets to like put my name,like it just has like the blank, like
brackets that chat GPT gives you.
And I'm like, guys, come on.
I can't trust you.
(23:41):
This is The funny thing
Sundas Khalid (23:42):
is like all of them were using the same structure, like
how in the world you all came togetherand just use the same structure.
Like this section is going to havethis, this is going to be this section.
It was just crazy.
Like, please, if you're like jobsearching, please don't use like raw chat
GPT output, like you're just risking.
So your, your application by doing that.
Avery (24:01):
I love it.
Okay.
Thanks for your AI tips.
I appreciate it.
Uh, let's talk some more about financialliteracy, because you said if you'd
never been at Google, you may havenever gotten to financial literacy.
You cover, you cover a lot in yourcontent, um, which is important, right?
Because.
As much as you and I love data andeveryone else, we probably wouldn't
be doing what we're doing right nowif it wasn't for the fact that like,
(24:22):
we want to be like financially secure.
Um, and I love howtransparent you've been.
You've done like a whole like 10year salary, um, like documentation
of like where you started.
It was like something like 40, 000 to likeover 500, 000 in the last like 10 years.
So what made you like, what waslike the thing that made you
get into financial literacy?
Sundas Khalid (24:40):
Yeah, no, that's a great question.
Um, I think it all started when Iattended this one talk at Google
and it's actually on their YouTube.
Um, this was by an author called,uh, his name is JL Collins.
He wrote The Simple Path to Wealth.
The book is really popular now.
Uh, but basically he came to oneof the Google talks and I ended up
(25:02):
attending, which I wasn't planning to.
And the way he talks is like, he talkslike he is like your uncle and he's like
trying to explain you like, what are.
What is investment?
What you should be investing in?
What is a retirement account and whatnot?
Ended up buying his book,ended up reading it.
And that's where like my, like that,me attending that like 30 minute
talk had Google basically inspiredme to get into financial literacy.
(25:26):
After that, like ended up reading RamitSethi's book, I will teach you to be rich.
Like these two books combined literallygave me everything that I needed to know.
And the funny part is up untilthis point, like I was in the
industry for about six years.
At Amazon, I had no idea thatAmazon offers 401k match.
I never really invested in 401k.
I left that 401k match on the table.
(25:47):
And I had all the moneyin my savings account.
Like all I knew was savings.
So I just kept saving.
So every time I went to my bankaccount, like the bank tellers,
the managers would come out andthey would be like so nice to me.
They were like, whydon't you come sit here?
I was always wondering like,why are they so nice to me?
And after I became financialliterate, I realized like they were
nice to me because nobody keeps.
(26:08):
That much money in their bankaccount, like people invest anyways.
So that led me to like openly talkingabout financial literacy because
there are many people like me whodon't fully understand like how to
actually make your money work for you.
Like, I don't have any like certificationsor like, um, what is the accolades to say?
Like I'm a financial educator.
Like I'm just sharing what I am doing.
(26:30):
And that's what I started doing.
Like I started sharing like whatI'm doing, like, this is what
I'm reading right now, this iswhat I'm investing in right now.
And it turns out like I have inspired alot of people to like, become financially
literate, like two books that Imentioned, like I've shared with thousands
of people, they have read it too.
And eventually, uh, I was like, okay,how can I like make more impact?
Because I'm so passionateabout this topic.
(26:50):
And that's when in like 2021, Idecided that, okay, I'm going to like
volunteer my time to help other peoplenegotiate their salaries because.
Another story, which I cover in detailin my course, but basically when
I got my data engineer offer fromAmazon, it was way, way, way below.
Just to give you an idea, my firstyear salary was 65, 000, which is
(27:12):
for a data engineer role based inSeattle, which is high cost of living.
Anyways, that led me to being on a pathto figuring out what my market rate is.
Eventually when I learned all those thingsfor myself, I wanted to help other people.
So then in 2021, I startedvolunteering my time.
If somebody had an offer, likeI'll go and basically help them
like negotiate their offer andgive them strategies and whatnot.
Eventually I realized like that isnot scalable with a full time job.
(27:35):
I cannot just get on a call witheverybody, uh, to kind of like
give them consulting and whatnot.
I'm, I don't know if you've everdone like one on ones, but like
those are difficult to scale.
Avery (27:43):
I did 250 last year.
Sundas Khalid (27:46):
I'm on the death.
How do you do that?
Avery (27:49):
Uh, I did, I tried, yeah, it's hard.
It's really hard.
Yeah, I totally get it.
Sundas Khalid (27:54):
Yeah.
It's hard.
And you hit a limit at some point.
You were like, okay, there'sno way I can do more.
So then I was like, okay, I, howcan I like continue scaling this?
So that's when I ended up building thecourse that I have right now, which
is on salary negotiation, where Ilike share all the tips and tricks on
how somebody, anybody can learn, uh,salary negotiation strategies and skill
and can negotiate their own salary.
(28:16):
The course that I have is likespecifically focused on tech because
that's what my specializationis like, I guess my area is, but
yeah, happy to talk more about it.
I actually have a special discount,a coupon code for your audience.
So, um, yeah, and I'll share it with you.
You can, it's a, it's Avery20.
So like if you go to the website, whichyou can link here and use the coupon
(28:37):
code Avery20 to get 20 percent off.
Avery (28:41):
Okay.
Awesome.
Yeah.
I, you're being humble because, um, likeobviously like, like, uh, you, you've been
really good at, great at this, but likein one year you helped 50 different women.
Negotiate like 1.
4 million of extra incremental salary,not like total salary, incremental salary.
Um, and I did the math.
(29:01):
I'm pretty sure.
I think that's like 30,000 per person on average.
And I just want to like highlightthis to, to everyone listening that
like some of this is offering this,obviously it's, it's paid, but like.
And you might not get 30, 000 out of it,but you're going to get a lot out of it.
And the coolest part about salarynegotiation, in my opinion, and
Sundance, you're the expert here.
So correct me if I'm wrong.
The majority of the time, the worst thingthat happens is they say, no, sorry.
(29:26):
Right.
And the best thing thathappens is they say yes.
And the most likely thing is theymeet you somewhere in the middle.
And so like, in my opinion, correct meif I'm wrong, like there's not really
a downside for asking for more money.
The majority of the time.
Sundas Khalid (29:39):
Yeah, like unless until the recruiter says this is the last and
final offer, like you, you wait for thosewords and unless until the recruiter says
the last final offer, it is not a finaloffer, basically when they give you the
first offer there is still room, theyleave room for negotiation because a lot
of people do negotiate even though likethere was a study done like 50 percent
of the people Don't negotiate, which issurprising, but when the recruiters are
(30:02):
giving you that number, they are leavingroom for negotiation for you to ask more.
And when you accept that andbelieve me, like I've been there.
Uh, when you go through thatrigorous job market, like that we
are currently in, and then you gothrough like so many interviews
and then you finally get an offer.
You're like, thank GodI'm so done with this.
I'm like, so over it.
Like the first offer or likewhatever offer number you are on.
(30:23):
You get it and you're like,I want to just sign it, lock
it, and like be over with it.
Just like hold on a little more andjust stay patient in that stage.
Because chances are it could be justyou simply asking the recruiter and they
might come back and say like, yes, I canincrease your compensation by this much.
Don't accept without asking,um, and listen for those
words, a last and final offer.
(30:45):
Cause, but even with that, like there'sa lot of strategies that you can use,
for example, like competing offer, uh,the market research tools and whatnot.
So there are ways to negotiate, uh,but don't accept your first offer.
Avery (30:58):
I just think this is so cool that you're doing this because, um, I honestly
think it's one of the best investmentsanyone can make because when you've gotten
to that point where they're literallysaying, okay, we're going to offer you.
Like they don't want, they're not,they're not looking to get rid of you.
You're looking to hire you.
And so if you ask for more money,it's not going to be, they're
not going to be like, Oh crap.
Like, nope.
See you later.
You're not getting this job offer.
(31:18):
Like, as long as you're likereally appropriate and you, you
do do it professionally, you'reprobably going to get something.
You might get nothing,but at least you can ask.
Um, and the other thing I want tojust really highlight, you know, going
back to the financial literacy thing.
Is, you know, let's say, let's say younegotiate and let's just say you get,
let's just make it somewhat smaller.
Let's make it 3, 000 instead of 30, 000.
You have to realize that that 3, 000is going to be there every year, the
(31:43):
rest of your life for that salary.
Um, so it's basically compounding.
So like, let's say you negotiate3, 000, that's an extra 3, 000.
Like you might not get that raise fora year, for two years, for three years,
you might not get a 3, 000 raise.
And so you're getting that up frontand that's just going to compound
upon every single year, the restof your life that you're working.
So it's, it's almost like you're not doing3, 000 and I'm really bad at compound
(32:07):
interest and, and stuff like that.
But like, yeah, 3, 000 is actuallylike, you know, 20 years down the
line worth like something like 50,000 or something like much, much more.
Sundas Khalid (32:17):
Right.
And the, the, the part about like, askif you're able to get that 3, 000, for
example, let's say your compensationfor software was 100K and you end
up negotiating and now it's 103K.
So let's say next year when it'sperformance review, like you are given
the annual raised and most big, major midsized companies, you're giving even like
the smaller companies, like the, yourannual raise is based on your base salary.
(32:42):
So.
You're going to be given, given upcertain percentage, let's say 10
percent raise on that 103 numberinstead of the a hundred number.
So like it does add up eventually.
Avery (32:52):
Yeah.
Which I think is incredible.
So, um, that's super cool.
You're, you're, you'reone of the best at it.
You know, you've, like you said in,in this video, we'll have a link to
it in the description down below.
You've gone from like 40, 65,000 to, you know, over 250, 000
to a lot more money than that.
Just in negotiation, you've donedifferent tactics of like, Oh,
you've gotten competing offers fromMicrosoft and, you know, Amazon
(33:12):
and all these different things.
Um, so I'm really excited aboutthis and I think, uh, people
can really learn from it.
We'll have a link in the show notes downbelow and you can use that coupon code.
Send us, thank you forgiving us to the audience.
That is super exciting.
Avery 20.
Um, and where else canpeople, people find you.
Sundas Khalid (33:27):
Oh, my God.
I'm everywhere.
Um, I was, I'm actually speakingat another, another live webinar
later today, and I'm like, trying tofigure out which social should I get?
So I'm literally everywhere.
I'm on YouTube, Instagram,TikTok, LinkedIn.
I just started a newsletterthis year, uh, on Substack.
So it's my full name.
You can use SundasKhalid on Sundas Khalid.
(33:47):
Um, LinkedIn on YouTube and then onInstagram and TikTok, you can find me.
It's Sundas Khalid, but there'san extra D, so like two Ds.
Uh, and you can link them belowand then my website, SundasKhalid.
com.
So if I ever, my accounts, mysocial media accounts ever shut
down, I will still have my website.
Yeah, I was going to say like,definitely subscribe to my newsletter
(34:08):
because that's something that is newto me and I'm planning to put a lot
of work into newsletter this year.
So, uh, if you want to hear from me, likein your inbox, like that's the way to go.
Avery (34:18):
Perfect.
I look forward to that.
Uh, I, I really enjoy, uh, all of a suddenthis is social medias, but specifically
I think her YouTube videos and herInstagram videos are really great.
Well, perfect.
We'll have all those linksin the show notes down below.
Sundas, thank you for coming on.
Sundas Khalid (34:31):
No, thank you so much for having me, Avery.
And I love that we have thatblue and red vibe going on.
It was perfect.
It worked out.
So
Avery (34:38):
fun.
Sundas Khalid (34:40):
Awesome.
Thank you, everybody.
Bye.
If you're breaking into data right now, you've probably seen one of
(00:03):
Sundus Khalid's videos with over 250,000 subscribers on both YouTube and
Instagram and absolutely killer content.
She's near impossible to miss.
She's worked as a data analyst, adata engineer, and a data scientist.
At both Amazon and Google.
But in today's episode, you'regoing to hear Sundance's
story in a whole new light.
(00:24):
You see, you know Sundance as the rockstar at Google and Amazon that she is.
But she's actually an immigranthigh school dropout who didn't even
speak English until later in life.
She didn't go to an Ivy Leagueschool, and she even started
her career later than most.
She is living proof that it'snever too late to break into
data, no matter your background.
So coming up, you'll hear Sundasnumber one data skill that you
(00:45):
need to learn no matter what.
Sundas Khalid (00:46):
I would have to pick a coding language, and it's gonna be
Avery (00:49):
s t.
If she likes being a data analyst, adata scientist, or a data engineer more.
I
Sundas Khalid (00:53):
don't want to pick.
I would say like d k ismy favorite for building.
And
Avery (00:58):
her crazy financial journey and what you can take from it.
Nobody
Sundas Khalid (01:01):
keeps.
That much money in their bank account.
Like people invest with
Avery (01:05):
that.
Let's get into the episode.
Sundance.
I'm so excited to have you onbecause you have such a unique story.
You're a high school dropout,immigrant child, and now you're an
analytics lead at freaking Google.
So how on earth did you get here?
Sundas Khalid (01:19):
First of all, thank you so much, Avery, for
having me on your podcast.
Um, and thanks for a great intro.
It's a long story, but I think likeyou summarize it really, really well.
I am a high school dropout and I'man immigrant and I was six years gap
between my high school and my university.
So when I look back now to like 10, 15years ago, I can't believe that I am here.
(01:40):
So it's been a long journey, a littlebumpy, but I am really grateful for all
the support that I've had throughout my.
career and throughoutmy education journey.
So a TLDR is that I went to Universityof Washington, went to business
school, and in the business school,I actually learned about data
analytics, databases, SQL and whatnot.
And that's where my love and mypassion started for the data field.
(02:04):
Then I just kept building on top of it.
I didn't have enough time to graduatewith a CS or a data science degree.
So I ended up building on my own,like continuing learning on my own.
So I'm a self taught dataengineer, data scientist.
Data analysts, likewhatever you want to call.
So it's been a long winded journey, butI'm so happy to be here and I'm happy
to answer and deep dive into any ofthese topics, uh, you'll let me know.
Avery (02:26):
Well, I'm super excited because I think there's a lot of people who
are watching this, who are like you,who might be immigrants to the U S who,
you know, maybe started school a littlebit later or later in their career.
And they're like, man, I don'tknow how the heck I'm going
to break into data analytics.
I think you're living proofthat like you can start late.
You can start disadvantaged.
Like you didn't even start speakingEnglish till later in life.
(02:46):
And you can still end up on thetop, which I think is really cool.
And also you didn't go tolike a brand name university.
You didn't study computer science.
You didn't study math.
You kind of just studied business.
Uh, what's been like the, the biggestthing for you in your career journey?
That's allowed you to, toget to where you're at.
Sundas Khalid (03:03):
Um, so I think like a couple of things that helped me
really daily, uh, in my career.
One is.
Knowing what I want to do andwhen I don't know what I want
to do, like I still kept going.
So when I started in my career, like myfirst internship, what am I was at Amazon?
Um, I was lucky enoughto get that internship.
It was really coincidentalbecause I learned about that
(03:24):
internship at a networking eventwhile I was going to school.
Prior to that, I was gettingrejected from all the internships.
From my experience, like I havealways been open to trying new things.
And Amazon is something that Ididn't want to try initially,
but I just jumped into it.
One of my best friends at that time,um, he actually worked at Amazon and
(03:44):
he was like, no, you have two kids.
There's no way you're evergoing to survive at Amazon.
I was like, no, I have to try itfor myself and I have to go for it.
I ended up going for it.
And that was ended up being oneof the best career decisions that
I've made because one, Amazontook a lot of chances on me.
Like it let me try outnew things, for example.
My first job was a data engineer,uh, which I like passed the technical
(04:05):
interview screen, but there was still alot that I needed to learn on the job.
So my teammates, my seniormembers on the team.
basically taught me a lot duringmy first job as a data engineer.
Uh, secondly, like one of the advicethat I got from my mentor early on
is, um, I couldn't figure out, I, Iwould always meet people and they were,
they were always like so passionateabout specific topics, specific area.
(04:27):
And I wasn't really likepassionate, passionate about it.
I think I was doing dataengineering at that time.
So I asked my mentor,like, what should I do?
I know I'm not like reallypassionate about something.
In particular, like I like dataengineering, but I don't know
if I want to do that long term.
So his advice to me was sometimesyou find what you're passionate
about and sometimes you don't.
And if you don't know what you'repassionate about, you still keep going
(04:48):
and eventually you'll figure it out.
So that's exactly what I ended up doing.
I did data engineering and Ifound a data scientist role.
And that, for me, like, this is, Iknew, like, that's the exact next
thing that I want to do and I pivoted.
Having the right mentors by my side,having the aptitude to like pivot and
learn new things has been like really,really, really helpful in my career.
(05:09):
And lastly, I, I, I would, I want tosay like luck definitely plays a role.
You being at the right place, righttime definitely puts has some,
there is like some luck involved.
Like it would be unfair if anybody comesto you and say like, it's all hard work.
It's not all hard work.
It's hard work you putting in the work,but also like you have to be at the,
sometimes you have to be at the rightplace, right time for things to happen.
(05:31):
I like to say,
Avery (05:32):
yeah, I like to say the, the harder you work, the luckier
you get a lot of the time.
Like, um, if we go back to, youknow, landing your first day at a
job, you're just a business student.
You've taken a few like it classesin, in your college career.
But at the end of the day,you're like a business major.
Like you said, with two kids,how the heck are you going
to start interning at Amazon?
Um, and you went to that networkingevent and I think that's kudos to you
because a lot of people wouldn't havegone to that networking event because
(05:55):
one, it's like just another thing to do.
Two, those networking events, a lotof the times they're very awkward
and you have to like go up and likepresent yourself to people and you're
like, hi, I'm Sundance and like, youshould hire me and stuff like that.
And so yes, like luck had a big part.
Like they had to be interestedin you at that networking event.
Um, but just like the fact thatlike you showed up, uh, I think is.
That's a lot of people don't.
(06:15):
And that's, that's the hard thing isit's uncomfortable to show up sometimes.
And then the other thing I want to say,uh, about you, Sundance, that I think
has really stuck out to me, uh, we'vegotten to meet, uh, in person for a
couple of days, uh, a year ago, and thenwe've also just gotten interact online is
like, you're a very clear communicator.
Um, like you're very good at likeknowing what you want to say and making
it very easy for the person you'retalking with to understand like, okay,
(06:38):
this is what's on this means thisis like what she's doing and this
is what I should do because of it.
I think that's played like ahuge role in your career as well.
Would you agree?
Sundas Khalid (06:46):
Um, I think that definitely I would agree with that.
And I have to give credit to Amazonbecause, um, Amazon teaches you a way
to like communicate in writing andin talking, like they're very direct.
When I left Amazon and I went toGoogle and I was like asking people
who were previously at Amazon and nowwork at Google, I was like, can you
give me advice, like, uh, tell me whatI need to do differently and their,
(07:08):
uh, their advice to me was that like.
Be a little less, um, I don't wantto say indirect, but like soften,
soften up the language a little bit.
So like when you say that,like I'm not surprised at all.
Like I can be very direct, yeah.
Not as direct as I would like to be,but I can be like very direct and crisp
and clear in terms of like what I want.
And I think that has helped meoutside of work, like in content
(07:30):
creation and like being on YouTubeand, uh, teaching people things.
So it's been helpful.
Avery (07:35):
I agree.
Yeah.
Your YouTube audience, your,your Instagram audience, I
think, uh, would agree as well.
Now, like you said, you started offkind of in this data analyst role.
And then you kind of pivoted todata engineering and then you kind
of pivoted to data scientists.
And so you've actually worked inlike the big three data professions
at both Amazon and Google.
So I'm actually curious, uh, which ofthese positions did you enjoy the most?
Sundas Khalid (07:56):
Um, okay.
So I wanted to say Youknow, it's a tough question.
I left data engineering, solike there has to be a reason.
I would say like, they're all myfavorite for different reasons.
I don't want to pick.
So I would say like data engineeringis my favorite for building.
Like you get to build things.
And this is like one of the things thatI miss about being a data engineer.
Like I don't build things.
(08:17):
I don't build like data pipelinesor platforms that other people use.
And I can, at the end of theyear, I can be like, Oh my God.
These are the number of people that usemy product or the pipeline that I use.
I think like data analysthas some aspect of it.
But like, I definitely miss that fromthe data engineering point of view.
What I don't miss is the on call.
So that's definitely another topic.
Uh, the data scientist world is amazing.
(08:40):
It's just so, so huge in the ambiguity.
I kind of like to have lovehate kind of like a relationship
with like the ambiguity.
But I really love that.
I can actually take an ambiguousproblem and solve it in data science.
Uh, when I was working at Amazonas a data scientist, one of my,
the ideas that I focused on wasA B testing and experimentation.
(09:00):
And the coolest thing about A Btesting and experimentation is that
it would be, like you would run, oneof, some of the tests that we would
run would be very small difference.
For example, you would changethe font color from red to blue.
And you will see like a huge shift incustomer behavior, uh, the purchases,
the orders, and so like things likethat, that I had previously not
(09:21):
thought about, like data sciencerole made me like think about that.
So I really like that aspectof it quite a bit, quite a bit.
In terms of the data scientistjob family, it's humongous.
Like you can be more on the machinelearning side, more on like the
product data scientist side, Iwould say like my favorite one is
definitely product data scientist side,because you get to mix both product.
So you kind of like a data scientisttimes a product manager in one role.
(09:45):
So you're able to like, think ofmore creative ideas and solutions.
As a product manager, but thensolve them, um, as a data scientist.
So I guess like I did pick my favorite.
Avery (09:56):
There you go.
It's data scientists.
You just had to talk it out, I guess.
Yeah.
Um, that's very cool.
I like that.
You talked about like, okay, yeah.
Data engineering is building data.
Scientist is like more experimentingand trying to figure out how we solve.
Real world problems with math.
And then data analyst is somewhere,um, in, in between now, obviously in
those different roles, you've probablybeen using different tech stacks, but
(10:19):
there's definitely some overlap as well.
I'm going to make you choose one again.
If you had to choose one tool you've usedthe most in your career, what tool is it?
Sundas Khalid (10:26):
Okay.
I would have to pick a codinglanguage and it's going to be SQL.
And I don't think it's a surpriseto anybody listening to this
SQL is regardless if you're adata engineer, you're a data
scientist or you're a data analyst.
You have to learn SQL and you have to.
Not even know it, the basics.
You actually have to know that vast levelif you really want to grow in these roles.
In terms of the tools, I would say likeeach role uses different set of tools
(10:51):
and they don't have anything in common.
So like, I'll stick withthe coding language.
Avery (10:55):
I like it.
I think, yeah, maybe that's not asurprise that, uh, SQL, it's like
the most in demand data skill in,and honestly, all three job families.
It seems like, you know, I thinkPython gets close for, for data
scientists, but It's, it's really SQL.
Okay.
So SQL is the tool you've used the most.
Do you, do you, do you have a tool thatyou like to use more than, than SQL?
Sundas Khalid (11:16):
You mean like a coding language or just like coding
Avery (11:19):
language or like Tableau or Looker, or I don't know, like, is
there like some tool you really enjoy?
Sundas Khalid (11:25):
I think the tool that I really, really enjoy is Google Collabs,
um, notebooks, uh, because they arelike so, uh, dynamic, like you can like
code in R, it's like similar to likeJupyter Notebook, but I guess like I
never really, really got the hang ofJupyter Notebooks, I've always been
like a Google Collab person, so I reallylove using Google Collab as like part
of my job, and what I love about it islike you can write any language, like
(11:48):
you can have one notebook and write somany different languages, to produce
the results and you can share that codewith just literally a link with somebody
else that who's going to like take overyour work or like scale it and apply it.
Avery (12:00):
That's huge in, in the workplace, because like, like you said, like
sometimes maybe you're the data scientistand you're writing the code, but you're
not necessarily the person who's going toput it to scale, or maybe you just need to
share it with your manager or some otherproduct owner or something like that.
Uh, but it's also big forthose of you who are listening.
Who haven't landed a data job yet, becauseif you ever do any projects in Python,
if you do it like in Jupyter notebook,you're not going to be able to share it
(12:22):
very easily and like doing it in Googlecollab allows you to like have a link
that you can send to a recruiter orhiring manager and it just makes like
your life easier in terms of sharingthe work that you've actually done.
You've been at Google for five years now.
Um, and, uh, for those of you thathave listened to send us on her
YouTube channel, um, you've, you'vemaybe heard some of her stories.
Um, I highly suggest checking it out.
(12:43):
We'll have a link in theshow notes down below.
One of the cool things that I thinkthat you've done, and we'll get
into negotiation here in a bit.
Um, but you, you know, you wereat Amazon, you actually used.
Like multiple teams at Amazon, not reallyon purpose, but to kind of compete for
you that allowed you to kind of geta little bit more advantageous roles.
And then you interviewed inthe past at like Microsoft
and got offers from Microsoft.
And that allowed you to, youknow, get some, some better
(13:05):
opportunities at places like Google.
Um, but you've been atGoogle for five years now.
Um, can you just tell us like whatyour role is and what do you feel like?
Um, like What you do on a day to day basisand what you feel like you've learned.
Sundas Khalid (13:18):
First of all, like I'm surprised that you watched all those
videos because like some of those,some of that information I know lives,
like the Microsoft offer lives in ourvideos somewhere deep, so I'm grateful
that you watched that, uh, definitelyanother story, like how I use my
Microsoft offer to like get more frommy employer, Amazon at that time, but
in like my current role, um, at Googleis primarily focused on Google search.
(13:41):
Uh, so like when you searchon Google, like you'll see.
Some ads.
So like I work in Google searchads and then there's another tab
that you will see like shopping.
So like the ads that yousee in search and shopping.
So that's the part of my, uh, that's partof my team and that's what I support.
So my work is primarily likefocused on like doing advanced
analytics, like experimentation.
Uh, deep insights and kind of likefiguring out what works and what doesn't.
(14:05):
Um, so it's like a, I would say like,it's a, it's a hybrid of data scientists,
product data scientists, and advanceddata analytics all merged into one.
My typical day to day depends onthe project that I'm working on.
So for example, uh, right now thatthe project that I'm working on that I
told you, like before this call, like.
It's a large scale project, andwe've been working on it for many,
(14:27):
many years, and it's currentlyin the implementation stage.
And while we're implementing, thingsthat could go wrong are going wrong.
So, my current project is figuringout, uh, there is a small traffic
that we launched, and I'm doingan investigation to understand,
like, what exactly is happening.
So, like, doing deep dives thereto, like, root cause the problem.
That's my current focus.
(14:48):
Last month, if you ask me what myday looked like, my last month,
my day was, um, my days wasfocused on my, on experimenting.
So we were running a lotof like sequential testing.
So I was doing a lot of like experimentanalysis, trying to understand how
different arms of the experimentshave performed and what decisions
we need to take and whatnot.
Avery (15:07):
Very cool.
That's, that sounds very cool.
It's so, it's, it's soneat to like hear that.
I like it.
Oh yeah, there is data scientistsworking on this product that I literally
use every single day, you know, andthey're improving the product based
off of what I do with the product.
So I think, uh, that's,that's really cool.
And like for the, for the years thatyou've been there, like, what do you
feel like you've taken away as likeyour number one, like piece of advice?
(15:28):
Like, for instance, ifyou were to go back.
To Sundance five years ago on day oneof starting Google, you actually have a
video, I think, where you did like theday one of Google or something like that.
Uh, if you were to go back andtalk to that Sundance, what
advice would you give her?
And what would you tell her that,that maybe you, you wouldn't
have realized or thought back?
Sundas Khalid (15:43):
So let's go back a bit in terms of like, when I was at Amazon.
So Amazon was my first job andI spent about six, seven years.
If you like count my internshiptime as well, my internship was
eight months long, which is likenot a typical internship time.
time.
So I always wanted to experience industryoutside because Amazon is all I knew.
So when I started looking for jobs, likeI had a few companies in mind that I was
(16:05):
interested in, and Google was one of them.
And let me just say that if I hadn'tjoined Google, or if I hadn't left Amazon,
I wouldn't know like what it's like, youknow, Experiencing different work cultures
and figuring out what I actually like.
I think one of the biggest learning forme personally is learning about like what
type of culture, work culture and workenvironment I want to be part of, uh,
(16:27):
what I need to look for in my next job.
So one of the big thingsthat I immediately learned at
Google or like noticed is theculture and how nice people.
Uh, for example, like I, my Nuglerorientation was in New York.
Um, and I was meeting some of my teammatesthere that I've never met before.
And they were like, where are you based?
(16:47):
I'm like, I'm, I'm in Seattle.
And their response was like, love that.
Love that.
I'm like in my head, I'm like,why are they saying love that?
It's, uh, I've never even met them.
And this is the firsttime I'm meeting them.
Maybe just, they're justtrying to say that to me.
And then.
Weeks past, months past, like thiswas like a normal people behavior.
And eventually it kind of likerubbed onto me as well, where
I picked up that language.
(17:08):
Um, so I would say like thebiggest learning for me has been
like just seeing how people firstculture actually looks like.
And I'll talk, I'll definitely talkabout like how Google has been an
inspiration or has like helped melearn, become financially literate.
Because if I hadn't joinedGoogle, I don't think I would.
I don't want to say ever, but like, Idon't think the chances of me becoming
(17:30):
a financially literate person wouldhave happened if I hadn't joined Google.
So the number one thing definitelystands out is like the culture and
people like, uh, Google has some ofthe nicest people that I've ever met.
And what I like to tell myfriends is like a different world
inside that everybody's just.
Um, really nice to talk to.
It's
Avery (17:49):
it's
Sundas Khalid (17:50):
pleasant.
It's always pleasant.
Just so, I
Avery (17:52):
mean, I think they give those vibes off like, uh, like the
campus seems fun and like playful.
I've seen some of likevideos and pictures there.
And, uh, I mean, like even likethe logo feels a little bit, maybe.
More playful than other companies.
And it is, I think what you said is reallyimportant that like, you need to go out
there and try different companies, um,and maybe even different industries.
Because, uh, what I found in mycareer is I started my, my data
(18:15):
career at a really small biotechstartup that had like 15 employees.
I love them.
Shout out to vapor sense, butlike, you should have seen my desk.
Like it was, it was kind of like abox basically, like in a closet and
uh, like I didn't have nice equipment.
And so when I got an offer to go toExxon mobile, uh, at this huge campus
down in Texas, like this awesome sitstand desk, I was like, Hey, I need
(18:35):
to try that and see what it was like.
And then I got there and I was like.
Crap.
I hate working for a 70, 000 or not70, 000, 70, 000 person company,
uh, in manufacturing and I wouldnever, I would have never known that.
And I'm glad I still did it because Iwould have always been like, well, what
if I like working for a big company thatgives me nice perks, but I actually,
like when I was there, I realized, crap,I want to go back to like the rag tag
team, you know, of like a small company.
(18:57):
And then I started my own company.
Now I'm a company of one and I like that.
Right.
So, um, I think it'sreally cool that like.
At the end of the day, like we'reall, we're at work for, you know,
40 hours a week, most of us, right?
Something like that.
Maybe more, maybe less.
Like we want to be doing something weactually enjoy with people we enjoy
in an environment that we enjoy.
And obviously the money is important,but like if, if you paid me a bajillion
(19:19):
dollars, okay, maybe not a bajillion,but if you paid me a lot of money
to do something I didn't enjoy.
A million!
Okay, if you paid me a milliondollars, but I hate my life,
I don't know if I would do it.
If you paid me a billion, I'm probablyin, but a billion, I don't know.
Sundas Khalid (19:31):
Listen, you get that million, you work for
a year, and then you retire.
So.
Avery (19:34):
Perfect.
There you go.
So obviously one thing thatpeople are really interested
in is like this new wave of AI.
Do you have any tips on like for peopleof how they could be using AI at work?
Sundas Khalid (19:45):
Yeah, um, you know what?
That's a great question because AI islike the new hot topic and literally
anyone, everyone is talking about it.
So if somebody in this world who doesn'tknow what ChatGPT, Gemini or any of
the generative AI tools are, I don'tknow like who you are, please identify
yourself because Literally, everybodyknows it and have at least tried once.
(20:07):
Um, in terms of like using it atwork, I think it's becoming, uh, more
and more popular in the workspace.
Uh, so some of the things thatI have personally done and
use AI for is like coding.
So let's say if I'm writing a SQL codeor a Python code, and I can either,
there's like, um, There's an AI built inthat can help me like finish the code.
(20:28):
I think GitHub AI, what isGitHub's version called?
Avery (20:31):
Copilot is it?
Copilot,
Sundas Khalid (20:32):
yeah, literally basically Copilot and all of these other tools
that like helps you finish coding.
So like coding is definitelyone of the use cases.
So if you are a coder, definitelytake a look at, look into that.
One of the things that I'm really,really proud of is, like, I wrote
a document in less than 30 minutes.
It's a two page document using Gemini,which turned out to be really good.
(20:52):
I did not use the exact copy ofthe Gemini, just for the reference.
Um, I basically got an outline, gotsome, some sections to fill, and then
I turned it into my own language.
Sometimes when you stare at a blackpiece of paper, it's just difficult
to start, so having Gemini builtin, I'm able to kind of like, have
it start, and then I like, I, Ibasically jump in and like, take over.
(21:15):
Then email writing and email summarizing,like sometimes when you have like, long
You can literally use email that is builtinto like Gmail and other email tools to
like summarize the large thread and helpyou understand what exactly it is saying.
So it's like a great time saver.
The two, the last two that I want tomention is like summarizing Google Slides.
Sometimes I get access to likethese large decks that I really
(21:36):
do not want to go through.
So I will just ask Gemini tolike summarize these for me.
And then my last one, my favoriteone so far has been Notebook LM.
Um, I don't know if you havetried Notebook LM, but it's
literally, it's just, Just mindblowing what it's capable of.
You can basically actually did a YouTubevideo on this where I did a walkthrough.
I'm writing my next newsletter is goingto be about notebook LM as well, but
(21:57):
basically you plug in your documents.
You can even linkarticles, YouTube videos.
Um, and you can ask it to like,uh, create summaries, uh, for you.
It's basically like your own tinyrag system that you have built using
NotebookLM that you can like askquestions that are like specific to
the documents that you have imported.
It can also create a podcast for you.
I mean, I can talk aboutit for a very long time.
(22:19):
I love NotebookLM, like one of theprojects that I mentioned earlier,
I'm actually using NotebookLM to likescale all the work to global teams.
Because notebook LM can literally, Ican import like the dozens of documents
that I have from last two years, um,and like build it one repository.
And instead of like somebody who is likeonboarding on this project, instead of
(22:39):
like reading through every document,they can just like ask questions to
notebook LM and like get an answer,which I think is really, really cool.
Okay, I'll tell you one thing.
I don't need to use a tool to figureout if you wrote something with chat
GPT or Gemini, like I can, I can readyour script for like 10 seconds, and
I'll know like you wrote something.
So recently, we're going off topic, butrecently, like, I was interviewing for
(22:59):
my personal assistant position and therewere like 500 applicants and after reading
those 500 applications, like, I kind offigured who wrote with ChatGPT, who wrote
with Gemini and like, what are they doing?
So, use it at your own risk.
But it's a great starting point, butit's not, it shouldn't be your end point.
So I won't be watching videosthat are just had deputy
(23:19):
scripts because I can tell,
Avery (23:20):
I like what you said earlier.
It's like a warm start.
You're not starting froma blank, blank slate.
Um, I know I've been hiring a lot recentlyand there's been multiple candidates,
I would say like close to 10 to 20%.
That forgets to like put my name,like it just has like the blank, like
brackets that chat GPT gives you.
And I'm like, guys, come on.
I can't trust you.
(23:41):
This is The funny thing
Sundas Khalid (23:42):
is like all of them were using the same structure, like
how in the world you all came togetherand just use the same structure.
Like this section is going to havethis, this is going to be this section.
It was just crazy.
Like, please, if you're like jobsearching, please don't use like raw chat
GPT output, like you're just risking.
So your, your application by doing that.
Avery (24:01):
I love it.
Okay.
Thanks for your AI tips.
I appreciate it.
Uh, let's talk some more about financialliteracy, because you said if you'd
never been at Google, you may havenever gotten to financial literacy.
You cover, you cover a lot in yourcontent, um, which is important, right?
Because.
As much as you and I love data andeveryone else, we probably wouldn't
be doing what we're doing right nowif it wasn't for the fact that like,
(24:22):
we want to be like financially secure.
Um, and I love howtransparent you've been.
You've done like a whole like 10year salary, um, like documentation
of like where you started.
It was like something like 40, 000 to likeover 500, 000 in the last like 10 years.
So what made you like, what waslike the thing that made you
get into financial literacy?
Sundas Khalid (24:40):
Yeah, no, that's a great question.
Um, I think it all started when Iattended this one talk at Google
and it's actually on their YouTube.
Um, this was by an author called,uh, his name is JL Collins.
He wrote The Simple Path to Wealth.
The book is really popular now.
Uh, but basically he came to oneof the Google talks and I ended up
(25:02):
attending, which I wasn't planning to.
And the way he talks is like, he talkslike he is like your uncle and he's like
trying to explain you like, what are.
What is investment?
What you should be investing in?
What is a retirement account and whatnot?
Ended up buying his book,ended up reading it.
And that's where like my, like that,me attending that like 30 minute
talk had Google basically inspiredme to get into financial literacy.
(25:26):
After that, like ended up reading RamitSethi's book, I will teach you to be rich.
Like these two books combined literallygave me everything that I needed to know.
And the funny part is up untilthis point, like I was in the
industry for about six years.
At Amazon, I had no idea thatAmazon offers 401k match.
I never really invested in 401k.
I left that 401k match on the table.
(25:47):
And I had all the moneyin my savings account.
Like all I knew was savings.
So I just kept saving.
So every time I went to my bankaccount, like the bank tellers,
the managers would come out andthey would be like so nice to me.
They were like, whydon't you come sit here?
I was always wondering like,why are they so nice to me?
And after I became financialliterate, I realized like they were
nice to me because nobody keeps.
(26:08):
That much money in their bankaccount, like people invest anyways.
So that led me to like openly talkingabout financial literacy because
there are many people like me whodon't fully understand like how to
actually make your money work for you.
Like, I don't have any like certificationsor like, um, what is the accolades to say?
Like I'm a financial educator.
Like I'm just sharing what I am doing.
(26:30):
And that's what I started doing.
Like I started sharing like whatI'm doing, like, this is what
I'm reading right now, this iswhat I'm investing in right now.
And it turns out like I have inspired alot of people to like, become financially
literate, like two books that Imentioned, like I've shared with thousands
of people, they have read it too.
And eventually, uh, I was like, okay,how can I like make more impact?
Because I'm so passionateabout this topic.
(26:50):
And that's when in like 2021, Idecided that, okay, I'm going to like
volunteer my time to help other peoplenegotiate their salaries because.
Another story, which I cover in detailin my course, but basically when
I got my data engineer offer fromAmazon, it was way, way, way below.
Just to give you an idea, my firstyear salary was 65, 000, which is
(27:12):
for a data engineer role based inSeattle, which is high cost of living.
Anyways, that led me to being on a pathto figuring out what my market rate is.
Eventually when I learned all those thingsfor myself, I wanted to help other people.
So then in 2021, I startedvolunteering my time.
If somebody had an offer, likeI'll go and basically help them
like negotiate their offer andgive them strategies and whatnot.
Eventually I realized like that isnot scalable with a full time job.
(27:35):
I cannot just get on a call witheverybody, uh, to kind of like
give them consulting and whatnot.
I'm, I don't know if you've everdone like one on ones, but like
those are difficult to scale.
Avery (27:43):
I did 250 last year.
Sundas Khalid (27:46):
I'm on the death.
How do you do that?
Avery (27:49):
Uh, I did, I tried, yeah, it's hard.
It's really hard.
Yeah, I totally get it.
Sundas Khalid (27:54):
Yeah.
It's hard.
And you hit a limit at some point.
You were like, okay, there'sno way I can do more.
So then I was like, okay, I, howcan I like continue scaling this?
So that's when I ended up building thecourse that I have right now, which
is on salary negotiation, where Ilike share all the tips and tricks on
how somebody, anybody can learn, uh,salary negotiation strategies and skill
and can negotiate their own salary.
(28:16):
The course that I have is likespecifically focused on tech because
that's what my specializationis like, I guess my area is, but
yeah, happy to talk more about it.
I actually have a special discount,a coupon code for your audience.
So, um, yeah, and I'll share it with you.
You can, it's a, it's Avery20.
So like if you go to the website, whichyou can link here and use the coupon
(28:37):
code Avery20 to get 20 percent off.
Avery (28:41):
Okay.
Awesome.
Yeah.
I, you're being humble because, um, likeobviously like, like, uh, you, you've been
really good at, great at this, but likein one year you helped 50 different women.
Negotiate like 1.
4 million of extra incremental salary,not like total salary, incremental salary.
Um, and I did the math.
(29:01):
I'm pretty sure.
I think that's like 30,000 per person on average.
And I just want to like highlightthis to, to everyone listening that
like some of this is offering this,obviously it's, it's paid, but like.
And you might not get 30, 000 out of it,but you're going to get a lot out of it.
And the coolest part about salarynegotiation, in my opinion, and
Sundance, you're the expert here.
So correct me if I'm wrong.
The majority of the time, the worst thingthat happens is they say, no, sorry.
(29:26):
Right.
And the best thing thathappens is they say yes.
And the most likely thing is theymeet you somewhere in the middle.
And so like, in my opinion, correct meif I'm wrong, like there's not really
a downside for asking for more money.
The majority of the time.
Sundas Khalid (29:39):
Yeah, like unless until the recruiter says this is the last and
final offer, like you, you wait for thosewords and unless until the recruiter says
the last final offer, it is not a finaloffer, basically when they give you the
first offer there is still room, theyleave room for negotiation because a lot
of people do negotiate even though likethere was a study done like 50 percent
of the people Don't negotiate, which issurprising, but when the recruiters are
(30:02):
giving you that number, they are leavingroom for negotiation for you to ask more.
And when you accept that andbelieve me, like I've been there.
Uh, when you go through thatrigorous job market, like that we
are currently in, and then you gothrough like so many interviews
and then you finally get an offer.
You're like, thank GodI'm so done with this.
I'm like, so over it.
Like the first offer or likewhatever offer number you are on.
(30:23):
You get it and you're like,I want to just sign it, lock
it, and like be over with it.
Just like hold on a little more andjust stay patient in that stage.
Because chances are it could be justyou simply asking the recruiter and they
might come back and say like, yes, I canincrease your compensation by this much.
Don't accept without asking,um, and listen for those
words, a last and final offer.
(30:45):
Cause, but even with that, like there'sa lot of strategies that you can use,
for example, like competing offer, uh,the market research tools and whatnot.
So there are ways to negotiate, uh,but don't accept your first offer.
Avery (30:58):
I just think this is so cool that you're doing this because, um, I honestly
think it's one of the best investmentsanyone can make because when you've gotten
to that point where they're literallysaying, okay, we're going to offer you.
Like they don't want, they're not,they're not looking to get rid of you.
You're looking to hire you.
And so if you ask for more money,it's not going to be, they're
not going to be like, Oh crap.
Like, nope.
See you later.
You're not getting this job offer.
(31:18):
Like, as long as you're likereally appropriate and you, you
do do it professionally, you'reprobably going to get something.
You might get nothing,but at least you can ask.
Um, and the other thing I want tojust really highlight, you know, going
back to the financial literacy thing.
Is, you know, let's say, let's say younegotiate and let's just say you get,
let's just make it somewhat smaller.
Let's make it 3, 000 instead of 30, 000.
You have to realize that that 3, 000is going to be there every year, the
(31:43):
rest of your life for that salary.
Um, so it's basically compounding.
So like, let's say you negotiate3, 000, that's an extra 3, 000.
Like you might not get that raise fora year, for two years, for three years,
you might not get a 3, 000 raise.
And so you're getting that up frontand that's just going to compound
upon every single year, the restof your life that you're working.
So it's, it's almost like you're not doing3, 000 and I'm really bad at compound
(32:07):
interest and, and stuff like that.
But like, yeah, 3, 000 is actuallylike, you know, 20 years down the
line worth like something like 50,000 or something like much, much more.
Sundas Khalid (32:17):
Right.
And the, the, the part about like, askif you're able to get that 3, 000, for
example, let's say your compensationfor software was 100K and you end
up negotiating and now it's 103K.
So let's say next year when it'sperformance review, like you are given
the annual raised and most big, major midsized companies, you're giving even like
the smaller companies, like the, yourannual raise is based on your base salary.
(32:42):
So.
You're going to be given, given upcertain percentage, let's say 10
percent raise on that 103 numberinstead of the a hundred number.
So like it does add up eventually.
Avery (32:52):
Yeah.
Which I think is incredible.
So, um, that's super cool.
You're, you're, you'reone of the best at it.
You know, you've, like you said in,in this video, we'll have a link to
it in the description down below.
You've gone from like 40, 65,000 to, you know, over 250, 000
to a lot more money than that.
Just in negotiation, you've donedifferent tactics of like, Oh,
you've gotten competing offers fromMicrosoft and, you know, Amazon
(33:12):
and all these different things.
Um, so I'm really excited aboutthis and I think, uh, people
can really learn from it.
We'll have a link in the show notes downbelow and you can use that coupon code.
Send us, thank you forgiving us to the audience.
That is super exciting.
Avery 20.
Um, and where else canpeople, people find you.
Sundas Khalid (33:27):
Oh, my God.
I'm everywhere.
Um, I was, I'm actually speakingat another, another live webinar
later today, and I'm like, trying tofigure out which social should I get?
So I'm literally everywhere.
I'm on YouTube, Instagram,TikTok, LinkedIn.
I just started a newsletterthis year, uh, on Substack.
So it's my full name.
You can use SundasKhalid on Sundas Khalid.
(33:47):
Um, LinkedIn on YouTube and then onInstagram and TikTok, you can find me.
It's Sundas Khalid, but there'san extra D, so like two Ds.
Uh, and you can link them belowand then my website, SundasKhalid.
com.
So if I ever, my accounts, mysocial media accounts ever shut
down, I will still have my website.
Yeah, I was going to say like,definitely subscribe to my newsletter
(34:08):
because that's something that is newto me and I'm planning to put a lot
of work into newsletter this year.
So, uh, if you want to hear from me, likein your inbox, like that's the way to go.
Avery (34:18):
Perfect.
I look forward to that.
Uh, I, I really enjoy, uh, all of a suddenthis is social medias, but specifically
I think her YouTube videos and herInstagram videos are really great.
Well, perfect.
We'll have all those linksin the show notes down below.
Sundas, thank you for coming on.
Sundas Khalid (34:31):
No, thank you so much for having me, Avery.
And I love that we have thatblue and red vibe going on.
It was perfect.
It worked out.
So
Avery (34:38):
fun.
Sundas Khalid (34:40):
Awesome.
Thank you, everybody.
Bye.
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