December 10, 2024 • 28 mins
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It’s not just about skills; find out what makes hiring managers say, “You’re the one we’ve been looking for.” Featuring hiring managers like Alex The Analyst, Megan McGuire, Jesse Morris, and Andrew Madson, the episode provides actionable tips and behind-the-scenes looks at what it takes to stand out in the data job market.
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⌚ TIMESTAMPS
00:25 Alex The Analyst: The Importance of Personality in Hiring
08:01 Megan McGuire: The Hiring Process from Start to Finish
17:06 Jesse Morris: Storytelling and Tenacity in Data Roles
23:21 Andrew Madson: The Value of Projects and Team Fit
26:27 Conclusion and Additional Resources
🔗 CONNECT WITH GUESTS
Alex Freberg: https://www.linkedin.com/in/alex-freberg
Megan McGuire: https://www.linkedin.com/in/megan-s-mcguire
Jesse Morris: https://www.linkedin.com/in/jessemorris1
Andrew Madson: https://www.linkedin.com/in/andrew-madson
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Avery (00:00):
As the host of the number one data podcast on Spotify, I've
had the opportunity to interviewa lot of cool people, including
a lot of data hiring managers whoyou'll hear from in this episode.
And they've given really great adviceon how to get hired in the data space.
In this episode, you'll hear the bestsnippets from those hiring managers
and get actionable advice on howactually to land a data job straight
(00:23):
from the hiring manager's mouth.
Let's get into it.
Our first hiring manager youmay have seen before on YouTube.
It's Alex, the analyst or Alex Freyberg,and he has really great hiring experience
from when he was at his corporate job.
And in this example here, I want youto pay attention to what he thought
mattered most when getting hired.
It's probably going to surprise youwhen you were hiring people, like what
(00:44):
was important in a candidate for you?
Like what was the first fewthings you were looking at?
Alex (00:48):
Yeah.
I'm going to be like brutally honestbecause I think people tend to sugarcoat
this process and the hiring process is.
A lot of people on like LinkedInor YouTube will tell you like
the sugar coated version.
I'm going to tell youwhat I truly looked for.
I was on a hiring teamwhen I was a data analyst.
I was the one who gavethe technical interviews.
And so that was my partof the hiring team.
And then you're right.
(01:09):
I became a hiring manager.
And so then as the hiring manager, I didthe whole process and usually brought
in like my boss as well for some likethe final interviews during the hiring.
The, when I was on the hiringteam during that process, we
were mostly hiring data analysts.
I eventually started when I was ahiring manager was doing developers,
database engineers, or data engineers,and then data analysts as well.
(01:31):
So that was a little bit different,but on the hiring team, just for
data analysis, we always looked forsomeone who had a good personality
and most people will tell you.
I've seen it online.
They're like, well, you know, as longas you have the right skills and you get
in there and you smile, you know, that'sa good, that's what you need to do.
I, I think when you're on a team,you really do look for someone who's
(01:54):
going to fit well with your team.
And so, yeah.
So, I always kind of gravitatedtowards people who are more outgoing,
and is that 100 percent fair?
No, I don't think so, but hiring,the hiring process isn't super fair.
And so, the people who are moreoutgoing, I tended to gravitate
to, and so did my whole team.
Our whole team was very outgoing, verysocial, and so we didn't want to, someone
come in and have a very different flowto them, or, or personality to them.
(02:19):
And so that's just like a brutaltruth, you know, people always
say diversity is like crazy good,but for personality, I think.
The, the, that piece of it is actuallythe flow of the team and how that, uh,
people gel together is really important.
The second thing we looked for is, uh,being able to articulate well, their
skills, abilities, and their experience.
(02:40):
And so oftentimes we'd have peoplecome in and SQL is really important.
When I was a, on the hiring team, SQL wasthe most important skill because we used
it like really in depth for a lot of ourprocesses and so people would come in and
I was like, well, tell me how you've done.
You know, data cleaning ortell me how you use SQL.
And if people can articulate reallywell, like here's how I use it.
(03:03):
They were just like, Oh, well, you know,I've, I've taken a few courses in my job.
I use SQL, but I don'treally use it that much.
And they would kind ofbeat around the bush.
And I'd be like askingreally pointed questions.
They couldn't articulate thosequestions that I would think is if
you've really used SQL well, youshould know how to answer those
questions because I can tell you.
Even at that time, I could belike, well, here's the process
(03:25):
that I would take to clean data.
Here's how I do that in SQL.
Here are, you know, hereare the exact steps.
That's what you need to be sayingand people would beat around the
bush and wouldn't want to say things.
And that was always a big red flag.
And then the last thing that Ithink we would look for is someone
who is technically proficient.
So I was the oneconducting the interviews.
(03:45):
We would always do some type ofwhiteboarding and then some type of
general technical interview question.
So the whiteboarding,you know, um, Uh, um, uh.
Uh, um, it really is the numberone thing that I, like this
is straightforward stuff.
And we were hiring at like the mid level.
So mid level SQL on theirresume for three years.
(04:06):
This should be a no brainer.
Like, like, This is like super simple,like, like just aggregating something with
a group by nothing crazy or just a simplejoint, just combine these two tables
and people would have trouble with it.
And that was an immediate red flag.
Like we couldn't hire them.
And so those three things Iwould say are the biggest things
that we look for and like reallyranked on during those interviews.
(04:27):
But if I'm being like completelyhonest, the personality thing
was like 50 percent of it.
If you have a good personality, thenthat like really puts you higher up.
And it's not just like I don't know,personality is very objective and so
it's hard to describe, but just somebodywho's more outgoing, very friendly.
That is like kind ofwhat we were looking for.
The being able to articulate and thetechnical interviews is the other 50%.
(04:51):
So those two things were stillvery important, but if they looked
like they were very teachable.
If they looked like they were likereally driven and we were like, you
know, they may not be where we wantthem today, but I was like, that
person will be good in like a month.
We would still hire them.
And we did that for one of ourbusiness analysts who we hired, um,
who were, he kind of knew SQL, buthis job wasn't as intensive as Eagle
(05:15):
for his, that, for that position.
So we were like, Hey, let's,Let's hire him cause he would
fit really well with our team.
And we trained him like I, he was mymentor, my, my, my mentee on my team.
So I trained him in SQL and he,within like a month he was up and
running and I didn't really haveto help him that much anymore.
So again, it was like that trainabilitypiece, the, um, the attitude,
the, uh, how driven they were didplay a big role in who we hired.
(05:39):
So I know I was long windedon that, but they, you know,
that's a, that's a really tough.
Process to our to talk about, you know,
Avery (05:44):
it is I think you did.
I think you did really well And I don'tI mean although it was you know, you did
talk about maybe the extrovert versus theintrovert I don't think it was too brutal
I think I think it's like an opportunityyou have a good personality and you
maybe aren't the best technical personon planet Earth You still have a chance.
Alex (05:58):
Yeah.
Well, I talked to a lot of people whogive me that feedback They're like,
well, I'm a really big introvert Iget really nervous and it's true and
they're like, how can I get past that?
and so So there arethings that you can do.
I really believe in practicingbefore interviews, mirror, looking
in a mirror and practicing smiling.
Cause believe it or not, I wasthat person back in interviews.
Um, I used to be very, very nervousand very scared for interviews.
(06:22):
Um, I used to be much moreintroverted than I am now.
I've worked through a lot of that as Igot into the workplace just by having
to, in order to like succeed on teams.
But I used to be very, very, very nervous.
And, and so what I would do isI'd practice in a mirror and then
my, I'd practice with my wife.
And so she'd be like, Oh,you're doing that weird, you're,
you're doing this weird thing.
And she'd be really honest with me.
(06:44):
And so I needed somebody whocould give me that feedback.
And that helped immensely in interviews.
So I'm kind of, I feel like I canpoint even to myself as like a
testament of someone who, who got overthat and was able to push through.
And then I was really able to.
Understand, like I have todo that in order to really be
successful in an interview.
Avery (07:02):
All right.
So maybe not exactly what you expected.
Personality really matters.
And I think that'sactually a positive thing.
Of course you have to have the skillsthat is like the bare minimum, but your
ability and your personality can actuallyset you apart from other candidates.
Now you might be thinking,well, that's great.
And like I said, I think it's a positivething because it means that there's
room for all of us in the data world.
(07:24):
Some of you guys will be thinkinglike Alex said, Oh, I'm introverted.
I don't have that great ofa personality or I'm kind of
scared to share my personality.
And I don't think you haveto become extroverted.
I'm actually an introvert,believe it or not.
But I think really practicing inthose interviews and at least just
coming off very personable in theinterviews is really important.
I love what Alex said about the mirror.
I actually built a software.
(07:45):
It's called Interview Simulatorthat lets you practice.
Interview questions, uh, with thehiring manager in front of you, like a
mirror, and you actually record yourselfand get feedback on your responses.
If you want to check that out,it's at interview simulator.
io or I'll have a link inthe show notes down below.
Okay.
Our next hiring manager is Megan McGuire.
In this snippet, she's going towalk us through what it's like
(08:06):
to actually hire someone in thedata world from beginning to end.
She'll talk about posting the job,how many applicants she got, how many
people The recruiter talked to howmany people she talked to as the hiring
manager and kind of what the nextsteps and how they ultimately hired
someone who actually didn't have allthat traditional of a data background.
Let's take a listen.
Okay.
So you write this jobdescription, you hand it over HR.
(08:28):
They, they post it somewhereon the internet somewhere and
applications start to come in.
So can you walk us through howmany applications, how long you,
maybe you guys have the job openand how many applications you got?
Megan (08:40):
Yeah, I think we had this role listed for like a week.
We didn't give it long, because wegot 285 applications within a week.
Honestly, when I looked at them, andI looked at every single one of them,
like, looked at every resume, probablyabout 70 percent of that applicant pool
could have been successful in the role.
Again, it's an entry level role.
A lot of this is about what you'reable to learn and like what you've
(09:03):
shown some promise in so far.
So yeah, most of these people honestlycould have done pretty well in the role.
So that makes it reallyhard to narrow down.
Honestly, when I hire a senior analyst,that's a lot easier because I can go
through and see that like, you don'thave the body of experience to support
that you've done this for a long time.
You don't have the portfolio.
You don't have the projects.
When I'm looking at a junioranalyst, I assume you're not
(09:25):
going to have those things.
So I have to parse out on alot more stringent criteria.
So if you don't have experience inthe tech stack that I'm looking for,
285 applicants, if only half of thosehave experience with Tableau, which
is what we use as our visualizationtool, I'm going to talk to the half
with Tableau before I talk to theother half with Power BI or Looker.
(09:46):
You have to prioritize on thesethings just because there's a
lot of people coming through.
So out of that 285, I think wehad 12 talk to our recruiter.
That's our next stage iswe do our recruiter screen.
I'm a big believer in the hiring process.
Like I'm not going to ask you todo a technical screen before we've
put some time forward to you.
We need to have thatsort of give and take.
So you talk to ourrecruiter at that stage.
(10:08):
After that, we had.
Five candidates exit because theyeither didn't respond, or location,
or salary requirements didn't line up.
So then we had seven candidatestake our code assessment.
We do a SQL test on CodeSignalto review candidates skills.
I really enjoy having something,like, technically grounded, where I'm
able to see the code you can write.
(10:29):
It doesn't really work well to do, like,a quick Tableau assessment, but SQL's
such a core skill, and it's really easyto test with a lot of SQL questions.
We're doing some grouping.
I think.
There might be a windowfunction question on there.
So at that stage, actually everybodypassed our SQL interview, but we
did have one candidate accept adifferent offer at that stage and exit.
(10:49):
Yeah.
Our average completion timeon that stage was 24 minutes.
My goal is also to keepthat stage pretty short.
I don't want to ask youfor like a six hour test.
You're applying for lots of jobs,especially at the entry level.
I'm not trying to keepyou for many, many hours.
The stage actually that we movedto was my hiring manager interview.
And in that stage, I'm asking usuallysome more problem solving questions.
(11:11):
So I'm going to ask you about somethingin your portfolio, something that you've
gone deep on, and ask you things like,how would you expand that project?
What else are you curious aboutthis project that you might've
worked on in your portfolio?
If you were rebuilding it, like, whatwould you do differently this time?
What other data would behelpful for driving decisions?
Those sorts of questions to dive deep.
Again, like, I'm not asking you aboutall of your experience in data analytics.
(11:34):
I assume that you don't have thatapplying for an entry level role.
I talked to six in the higher edgerstate and four of them went through.
The biggest gap for the two candidateswho exited there, I think was like
visualization and data exploration skills.
So then we moved into the teamtechnical interview where I have two
of the senior analysts on my team gothrough much more technical questions.
(11:55):
So in that stage, you're going tosee like, let's walk through your
portfolio project and talk about like.
How you build this in Tableau, you putsomething on Tableau public, we're going
to talk through the stages of building it.
So they're going to be vetting yourtechnical skills with a lot more detail.
This shouldn't be a scary stage.
Just feel confident speaking to thestages of not only how you did things,
(12:15):
like we're not going to ask whichbutton did you push, but think about
the methodology and why you choseto build something a certain way.
So like, if you chose to doa calculation in SQL versus
in a data visualization tool.
Why did you do that?
And how did you goabout figuring that out?
Those are going to be the sortsof questions to talk to there.
Avery (12:34):
Okay.
Awesome.
So then you're, you're analysts, you'rekind of doing this like team interview.
Now let me ask you this.
I mean, they've done this probably a fewtimes, maybe in their careers, right?
Do you give them questions?
Do they come up with their own questions?
Megan (12:47):
They come up with their own questions.
I talk to them primarily about the goals.
This is very similar to my managementstyle in general is I want to
talk to them about the goals.
What are we trying to find out?
To bring it all back into the data world,interviewing is a form of getting data.
This is a means of data collection.
So I talk to them about like,what do we want to learn about
the candidates at this stage?
And I will help them with writingquestions if they need it.
(13:08):
But for the most part, I'mtelling them like, I want to learn
about their technical skills.
I want to learn about how theygo about solving problems.
I want to learn about howcomfortable they feel in this system.
And you should be able tocome back and tell me about.
After that, that'sactually our last stage.
So we do that technical interview andthen I'm reviewing all of the feedback.
So our system.
As we collect scorecards afterevery interview, and then I have
(13:29):
access to review all of them.
So I can see something that's beenscored relatively objectively across
every interview and every candidateand sort of evaluate how that adds up.
So that'll be scoring onthings like technical skills.
How are your SQL skills?
How are your Tableau skills?
But it'll also include things likeproblem solving and other soft skills.
How are you as a communicator?
And I can evaluate against all of that.
(13:50):
In this case, I had two candidatesthat I was sort of debating
between in the final stage.
And then I made the call onwho to extend an offer to.
Really the differentiator forthe candidate who got the offer
is we're an education company.
We're here to help people upskill,learn data analytics, was that she
had prior experience in education.
So all of her technical skills were great.
Her communication skills were great.
(14:11):
Her portfolio was great, but I hadmultiple candidates who met all of that.
So her differentiator was reallythat education experience that
was really helpful for us.
It was something that sether apart and made her like
the perfect candidate for us.
Avery (14:25):
And I want to emphasize that here.
I work with a lot of teachers whowant to get into data analytics
and a lot of them are fearful.
Hey, I don't have a technical background.
I come from an unusual background,but in this case, that non technical
background, the unusual backgroundwas actually kind of the superpower
that got her the job or him the job.
Megan (14:42):
Yeah, like it's super, super helpful.
I can combine that portfolio,combine all the things that you've
learned about data analytics withthe other things that you know.
Somebody out there is makinged tech software that needs
to be sold to teachers.
Like you understand teaching, youunderstand the education world.
You can apply that knowledge todata analytics in that setting.
Be the perfect candidate for thatcompany rather than a pretty good
(15:04):
candidate for a whole sea of companies.
And the same could be applied again forlike, if you've got retail experience
or customer service experience, youmight look at a customer service
analytics role, which there are plenty.
Take your prior experience in customerservice and apply that to analytics.
I did it myself.
Like that's how I got intoanalytics was I studied healthcare
in my undergraduate program.
(15:25):
And I took an analytics roleat a healthcare company.
So when you can sort of combinethose things, it makes a much
more powerful profile, makesyou a much stronger candidate.
Again, like you don't haveto be okay for everybody.
Okay.
For everybody will get you a lot of likelooks, but you'll get the offer more when
you can find a way to make yourself likejust right for one company, those things.
Avery (15:46):
Okay.
How awesome was that behind the scenes?
I'm going to it's like to actuallyhire someone in the data space.
285 applicants just erased half ofthem because they didn't know Tableau.
That's why it's so important tolist all data skills pretty much
on your resume and your LinkedIn.
12 spoke to a recruiter andout of those 12, only 7 made
it to the next stage, which wasactually a SQL little coding test.
(16:09):
Everyone pretty muchpassed the coding test.
One person got offered ajob and so dropped out.
So six people talked to thehiring manager, which was Megan
in this case, kind of a behavioralinterview like you heard.
Out of those six, she kicked twoout after that and finally had
four interview with her team.
The team was part of the process,which I think is really neat.
And it goes back to what Alex wastalking about earlier, how you
(16:30):
really do need to mesh with the team.
Well, out of those four, two of themkind of stood out, but that couldn't
really choose between the two.
And the benefit of the doubt went tothe person whose domain experience,
like their past experience that wasn'tdata related would help the team.
In this case, it was someonewith an education background, and
this was an education company.
And so that's who they endedup hiring, which is awesome.
(16:51):
And I think for you, you shouldreally be thinking about.
You know, how can I use mydomain and my previous experience
to help me land my next job?
Hopefully your hiring manager is asgood and as kind as Megan, because I
think she hired very well in this case.
The next hiring manager we aregoing to hear from is Jesse Morris.
And I want you to pay attention towhat he thinks is most important
(17:12):
in the hiring process, because onceagain, I don't think you're going
to be able to guess what it is.
When you've hired some of those entrylevel people, what stood out the most
to you in those hiring processes?
Jesse (17:21):
Yeah, that's a great question.
And I think, you know, if you takeanything from today's conversation,
I think it's around this.
And, you know, again,I think it gets lost.
You've got to be the most technical in theroom or, you know, your ability to build
a dashboard and make it a work of art.
You know, that's like the most important.
I actually don't think that's the case.
And I actually think, Avery, you andI talked about this about, like, how
a lot of teachers make great analysts.
(17:43):
And I think there's a lot of truthto that because ultimately when I
think when it boils down to it Really?
It's it's a couple of key things oneIt's the ability to tell stories and be
succinct and that is not that's not justa data skill set That's a life skill set
You know If you look actually my originalbackground is in sales like I actually
I should say my second job My firstjob was I was a data analyst and then
(18:05):
I realized I needed to get presentationskills and the ability to tell stories
So I went into sales for a few years.
And so I think, you know, that skillset,the ability, but I think you can get
that in a myriad of ways, you canbe a great writer, you can, there's,
there's so many different ways youcan get that skillset, but I think
that's such a big one, especially.
You know, a lot of my time is spentcommunicating to executives and
to leadership teams and to boards.
(18:26):
Like I spent a lot of time tellingstories to the board and that's really
key is that ability to kind of boilthings down and to here's the most
important and then you can work back.
Ultimately, like people, when they getcurious about data, that's when they
start asking kind of your next layerof questions and you, you can make
that, you can bring that curiosityof the life through storytelling.
The other one, which isprobably a little bit.
(18:46):
Less common you want here, but this issomething that just continues to even
today even with senior analysts Itdoesn't matter what level of analysts
you are but tenacity and mentaltoughness Wow, so that's a really funny
one I tenacity to me like in my world.
I work in these smaller calledstartup type Uh, technology companies.
And so we're moving at reallyfast pace, but we don't get
(19:10):
weeks to work on projects.
So if you work in any large corporatecompanies, you're going to get that.
And that's okay.
I think ultimately it's goodto know, like, what are, what
type of environment you're in?
And so if you don't work necessarilywell under pressure and some of these
things I'm about to talk about, that'sokay, then you're probably maybe better
designed to work at larger companieswhere you're given the freedom to like,
(19:30):
sit down and work on things for weeks.
The environment I work in,we're not given that time.
And so the ability to, you know,change prioritization on a dime, to
juggle nine different projects at once.
If you talk to my data analysttoday, like this is the reality.
Like we, this week, we came into theweek with a plan and by Monday afternoon,
you know, it was Monday morning duringour standup and by Monday afternoon,
(19:51):
that plan got halfway derailed, right?
And so it's a reprioritizationgame and that's not for everybody.
I mean, I think ultimately.
You know, that's a tough thing to wrapyour head around and not get frustrated.
And, and I think you and I talked aboutthis before, but it's that like knowledge
of, I understand what perfect looks likeor really phenomenal looks like, but
I also understand what good enough is.
(20:12):
And I think that skill set,that's a really important one.
And that's not like, you know, I'm goingto learn this by watching X, Y, and Z.
I think that's something that youactually have to work towards and
build up that, that mental toughness.
I actually think failure, you know,it's easy to look at a resume and be
like, Oh, all this stuff went great.
I was a founder, youknow, at a tech company.
Good for me.
I also failed at that tech company.
Right.
I learned a lot of things throughtrial and error and that I think
(20:35):
it's the same for all of us.
And so those would be some of thethings I think that really stand out
to me when you boil it down to likesome of the key pieces behind it.
It's an attitude, right?
Like it's that willingness to say,Hey, I messed up here and that's okay.
Like, cool.
What'd you learn from it?
How do we make it better?
How can I help?
But I think, you know, thoseare ultimately some of the, when
you boil it down to some of thethings that I look for, no matter
(20:57):
what stage you're at within it.
And then I think.
You know, on the specifically on thestarting out analyst in particular, you
know, I think just a, again, perspectiveis an interesting one, but did you have
a sales background or did you work for,I mean, maybe you're working in retail.
Did you work for Banana Republicduring college where you were
like, all of those things,perspective and data is everything.
(21:18):
And what I mean by that is like yourability to speak into it from the
person who's asking the questionor the departments or the leaders
that are asking the questions.
Right.
Because as long as you've got justthat various perspective, that
actually has a lot more value.
I think sometimes the technical even does.
Avery (21:32):
Yeah.
And I hope people just heard whatyou said, because I think that's
very impactful, you know, justto kind of rehash them a bit.
It's not necessarily how technicalyou are that lands you the job.
Because I think you said thisphrase when we first originally
talked that the technical stuff.
It's kind of expected.
That's like you, you haveit or you kind of don't.
Right.
And it's really your storytelling,your grit, your attitude that separates
(21:55):
you, which I think for all of youguys listening who want to be aspiring
data analysts, that should be reallyrewarding because you can have grit.
You can be, you know,you can be authentic.
You can try hard.
You can have passion.
You can become a good,you know, storyteller.
Those aren't like necessary, like youhave to be spending 25 years of your life
in SQL to know how to master everything.
(22:16):
Right.
That's really, in my opinion, enlighteningand refreshing to hear because it can be
like, I think most people take the datacareer job hunt way too skill heavy.
Of course, skills are important.
Right.
But like, they're not everything.
And I think you kind of just said thatbasically, they're not everything.
All right.
Hopefully you're catchingthe drift at this point.
It was a pretty similar themehere that your technical skills,
(22:37):
of course, they're important.
It's the bare minimum.
It's actually things like yourstorytelling, the ability to be
succinct that sets you apart.
And that's something you'reprobably thinking, Avery,
you're not very good at that.
I've listened to your podcast episodes.
I've watched your YouTube videosand you're not very succinct.
And it's true.
It's something I'm still working on.
And I think it's a lifelong journey, butonce again, this is like Jesse said, a
(22:59):
life skill, not necessarily a data skill.
And so that's something that we canpractice and we can get better at.
And no matter how technicalor how non technical you are.
It's something that you canimprove on every day and work at.
Jesse also brought up tenacityand mental toughness, and this
is something that we can all do.
One thing that I do is I actuallytake ice cold showers and baths to
try to increase my mental toughness.
(23:20):
Kind of weird, but it works for me.
The last hiring manager we aregoing to hear from is Andrew Madsen.
I want you to pay attention to whathe says because he kind of repeats
what has already been said, but headds one really important point.
Part, uh, that the other oneshaven't talked about as much,
and that is projects, which isthe P part of the SBN method.
Let's take a listen.
I wanted you to walk us through theidea of when you're hiring a data
(23:42):
analyst, you know, what's reallyimportant to stand out as a candidate.
What can these listeners do to To standout and the data analyst job search.
Andrew (23:51):
Yeah.
My thoughts on thishave evolved over time.
So the data analyst position hasjust grown and grown and grown as
our needs for quality data analystshave really permeated every industry.
So there's a lot of opportunitythere before when I was new at
hiring data analysts and I wasnew into data myself, I really was
focused on the technical skills.
(24:11):
I was looking at whatever my stack was,like we use Tableau, whatever it is.
And I was looking for applicantswho match that data stack.
That's how I began looking for applicants.
Now what I look for if I was hiring a dataanalyst, I focus much more on the person.
I look for somebody who's curious.
I look for somebody who's resilient.
(24:33):
I look for somebody who's goingto mesh well with the team because
data analytics is a team sport.
You know, one person who just isn'ta team player can really throw off
the whole dynamic and ultimatelythe work and the business insights
that we're trying to drive.
So less important to me is yourspecific technical skill set.
You know, if you know Tableaureally well and we're using Power
(24:54):
BI, that's totally fine with me.
But you're demonstratingthat ability to learn.
And some of the ways that youcan do that, like Avery always
talks about, are projects.
I love to look at projects.
I love to see interesting projects.
We've all seen the Titanic dataset,and I don't mind if you use that, but
I want to see something that you'reinterested and passionate about, and
(25:14):
I want to learn about that with you.
And then if we're interviewing, Ireally want you to tell that story,
because the ability to communicateas a data analyst is so important.
You know, I don't want to haveto go to the stakeholders and
explain what you were doing.
I want you to go and representyourself and present your insights
and build those relationships.
So if you can have something you'repassionate about, you uncovered some
(25:34):
insights and you can communicate those ina story and a narrative that's engaging.
Those are so important.
Those will really set you apart.
Avery (25:42):
Okay.
That's awesome.
A lot to unpack there.
I think we, as data analysts,candidates often over index.
On how much, you know, thetechnical skills matter and
the technical skills do matter.
There's people who are willingto take a chance on you and you
have to show them that you're morethan just, you know, some NPC.
I don't even know what thateven, what that means right now.
(26:04):
What does that mean?
A non role playing, I don't even knowwhat it means, but it's like a non player
Andrew (26:08):
character.
Yeah.
Avery (26:09):
Non player character.
You have to show some sort of passion,some sort of personality, some sort of
drive, some sort of like, And that canbe even your grit, your communication,
you know, what you like about projects.
And it's just interesting that we'vehad a couple of different data hiring
managers on the podcast now, and they'veall let off with a very similar message.
There you have it, folks, advicestraight from hiring managers on
(26:30):
how to land your next day at a job.
If you want even more On how toland a data job, I highly suggest
checking out my newsletter.
Every week I send you a tip that helps youtake the next step in your data career.
You can subscribe at datacareerjumpstart.
com or in the show notes down below.
And if you want even more help onyour data journey, consider joining
the data analytics accelerator,which is my ten week bootcamp to
(26:52):
help you land your first data job.
You can find that link inthe show notes down below.
As the host of the number one data podcast on Spotify, I've
had the opportunity to interviewa lot of cool people, including
a lot of data hiring managers whoyou'll hear from in this episode.
And they've given really great adviceon how to get hired in the data space.
In this episode, you'll hear the bestsnippets from those hiring managers
and get actionable advice on howactually to land a data job straight
(00:23):
from the hiring manager's mouth.
Let's get into it.
Our first hiring manager youmay have seen before on YouTube.
It's Alex, the analyst or Alex Freyberg,and he has really great hiring experience
from when he was at his corporate job.
And in this example here, I want youto pay attention to what he thought
mattered most when getting hired.
It's probably going to surprise youwhen you were hiring people, like what
(00:44):
was important in a candidate for you?
Like what was the first fewthings you were looking at?
Alex (00:48):
Yeah.
I'm going to be like brutally honestbecause I think people tend to sugarcoat
this process and the hiring process is.
A lot of people on like LinkedInor YouTube will tell you like
the sugar coated version.
I'm going to tell youwhat I truly looked for.
I was on a hiring teamwhen I was a data analyst.
I was the one who gavethe technical interviews.
And so that was my partof the hiring team.
And then you're right.
(01:09):
I became a hiring manager.
And so then as the hiring manager, I didthe whole process and usually brought
in like my boss as well for some likethe final interviews during the hiring.
The, when I was on the hiringteam during that process, we
were mostly hiring data analysts.
I eventually started when I was ahiring manager was doing developers,
database engineers, or data engineers,and then data analysts as well.
(01:31):
So that was a little bit different,but on the hiring team, just for
data analysis, we always looked forsomeone who had a good personality
and most people will tell you.
I've seen it online.
They're like, well, you know, as longas you have the right skills and you get
in there and you smile, you know, that'sa good, that's what you need to do.
I, I think when you're on a team,you really do look for someone who's
(01:54):
going to fit well with your team.
And so, yeah.
So, I always kind of gravitatedtowards people who are more outgoing,
and is that 100 percent fair?
No, I don't think so, but hiring,the hiring process isn't super fair.
And so, the people who are moreoutgoing, I tended to gravitate
to, and so did my whole team.
Our whole team was very outgoing, verysocial, and so we didn't want to, someone
come in and have a very different flowto them, or, or personality to them.
(02:19):
And so that's just like a brutaltruth, you know, people always
say diversity is like crazy good,but for personality, I think.
The, the, that piece of it is actuallythe flow of the team and how that, uh,
people gel together is really important.
The second thing we looked for is, uh,being able to articulate well, their
skills, abilities, and their experience.
(02:40):
And so oftentimes we'd have peoplecome in and SQL is really important.
When I was a, on the hiring team, SQL wasthe most important skill because we used
it like really in depth for a lot of ourprocesses and so people would come in and
I was like, well, tell me how you've done.
You know, data cleaning ortell me how you use SQL.
And if people can articulate reallywell, like here's how I use it.
(03:03):
They were just like, Oh, well, you know,I've, I've taken a few courses in my job.
I use SQL, but I don'treally use it that much.
And they would kind ofbeat around the bush.
And I'd be like askingreally pointed questions.
They couldn't articulate thosequestions that I would think is if
you've really used SQL well, youshould know how to answer those
questions because I can tell you.
Even at that time, I could belike, well, here's the process
(03:25):
that I would take to clean data.
Here's how I do that in SQL.
Here are, you know, hereare the exact steps.
That's what you need to be sayingand people would beat around the
bush and wouldn't want to say things.
And that was always a big red flag.
And then the last thing that Ithink we would look for is someone
who is technically proficient.
So I was the oneconducting the interviews.
(03:45):
We would always do some type ofwhiteboarding and then some type of
general technical interview question.
So the whiteboarding,you know, um, Uh, um, uh.
Uh, um, it really is the numberone thing that I, like this
is straightforward stuff.
And we were hiring at like the mid level.
So mid level SQL on theirresume for three years.
(04:06):
This should be a no brainer.
Like, like, This is like super simple,like, like just aggregating something with
a group by nothing crazy or just a simplejoint, just combine these two tables
and people would have trouble with it.
And that was an immediate red flag.
Like we couldn't hire them.
And so those three things Iwould say are the biggest things
that we look for and like reallyranked on during those interviews.
(04:27):
But if I'm being like completelyhonest, the personality thing
was like 50 percent of it.
If you have a good personality, thenthat like really puts you higher up.
And it's not just like I don't know,personality is very objective and so
it's hard to describe, but just somebodywho's more outgoing, very friendly.
That is like kind ofwhat we were looking for.
The being able to articulate and thetechnical interviews is the other 50%.
(04:51):
So those two things were stillvery important, but if they looked
like they were very teachable.
If they looked like they were likereally driven and we were like, you
know, they may not be where we wantthem today, but I was like, that
person will be good in like a month.
We would still hire them.
And we did that for one of ourbusiness analysts who we hired, um,
who were, he kind of knew SQL, buthis job wasn't as intensive as Eagle
(05:15):
for his, that, for that position.
So we were like, Hey, let's,Let's hire him cause he would
fit really well with our team.
And we trained him like I, he was mymentor, my, my, my mentee on my team.
So I trained him in SQL and he,within like a month he was up and
running and I didn't really haveto help him that much anymore.
So again, it was like that trainabilitypiece, the, um, the attitude,
the, uh, how driven they were didplay a big role in who we hired.
(05:39):
So I know I was long windedon that, but they, you know,
that's a, that's a really tough.
Process to our to talk about, you know,
Avery (05:44):
it is I think you did.
I think you did really well And I don'tI mean although it was you know, you did
talk about maybe the extrovert versus theintrovert I don't think it was too brutal
I think I think it's like an opportunityyou have a good personality and you
maybe aren't the best technical personon planet Earth You still have a chance.
Alex (05:58):
Yeah.
Well, I talked to a lot of people whogive me that feedback They're like,
well, I'm a really big introvert Iget really nervous and it's true and
they're like, how can I get past that?
and so So there arethings that you can do.
I really believe in practicingbefore interviews, mirror, looking
in a mirror and practicing smiling.
Cause believe it or not, I wasthat person back in interviews.
Um, I used to be very, very nervousand very scared for interviews.
(06:22):
Um, I used to be much moreintroverted than I am now.
I've worked through a lot of that as Igot into the workplace just by having
to, in order to like succeed on teams.
But I used to be very, very, very nervous.
And, and so what I would do isI'd practice in a mirror and then
my, I'd practice with my wife.
And so she'd be like, Oh,you're doing that weird, you're,
you're doing this weird thing.
And she'd be really honest with me.
(06:44):
And so I needed somebody whocould give me that feedback.
And that helped immensely in interviews.
So I'm kind of, I feel like I canpoint even to myself as like a
testament of someone who, who got overthat and was able to push through.
And then I was really able to.
Understand, like I have todo that in order to really be
successful in an interview.
Avery (07:02):
All right.
So maybe not exactly what you expected.
Personality really matters.
And I think that'sactually a positive thing.
Of course you have to have the skillsthat is like the bare minimum, but your
ability and your personality can actuallyset you apart from other candidates.
Now you might be thinking,well, that's great.
And like I said, I think it's a positivething because it means that there's
room for all of us in the data world.
(07:24):
Some of you guys will be thinkinglike Alex said, Oh, I'm introverted.
I don't have that great ofa personality or I'm kind of
scared to share my personality.
And I don't think you haveto become extroverted.
I'm actually an introvert,believe it or not.
But I think really practicing inthose interviews and at least just
coming off very personable in theinterviews is really important.
I love what Alex said about the mirror.
I actually built a software.
(07:45):
It's called Interview Simulatorthat lets you practice.
Interview questions, uh, with thehiring manager in front of you, like a
mirror, and you actually record yourselfand get feedback on your responses.
If you want to check that out,it's at interview simulator.
io or I'll have a link inthe show notes down below.
Okay.
Our next hiring manager is Megan McGuire.
In this snippet, she's going towalk us through what it's like
(08:06):
to actually hire someone in thedata world from beginning to end.
She'll talk about posting the job,how many applicants she got, how many
people The recruiter talked to howmany people she talked to as the hiring
manager and kind of what the nextsteps and how they ultimately hired
someone who actually didn't have allthat traditional of a data background.
Let's take a listen.
Okay.
So you write this jobdescription, you hand it over HR.
(08:28):
They, they post it somewhereon the internet somewhere and
applications start to come in.
So can you walk us through howmany applications, how long you,
maybe you guys have the job openand how many applications you got?
Megan (08:40):
Yeah, I think we had this role listed for like a week.
We didn't give it long, because wegot 285 applications within a week.
Honestly, when I looked at them, andI looked at every single one of them,
like, looked at every resume, probablyabout 70 percent of that applicant pool
could have been successful in the role.
Again, it's an entry level role.
A lot of this is about what you'reable to learn and like what you've
(09:03):
shown some promise in so far.
So yeah, most of these people honestlycould have done pretty well in the role.
So that makes it reallyhard to narrow down.
Honestly, when I hire a senior analyst,that's a lot easier because I can go
through and see that like, you don'thave the body of experience to support
that you've done this for a long time.
You don't have the portfolio.
You don't have the projects.
When I'm looking at a junioranalyst, I assume you're not
(09:25):
going to have those things.
So I have to parse out on alot more stringent criteria.
So if you don't have experience inthe tech stack that I'm looking for,
285 applicants, if only half of thosehave experience with Tableau, which
is what we use as our visualizationtool, I'm going to talk to the half
with Tableau before I talk to theother half with Power BI or Looker.
(09:46):
You have to prioritize on thesethings just because there's a
lot of people coming through.
So out of that 285, I think wehad 12 talk to our recruiter.
That's our next stage iswe do our recruiter screen.
I'm a big believer in the hiring process.
Like I'm not going to ask you todo a technical screen before we've
put some time forward to you.
We need to have thatsort of give and take.
So you talk to ourrecruiter at that stage.
(10:08):
After that, we had.
Five candidates exit because theyeither didn't respond, or location,
or salary requirements didn't line up.
So then we had seven candidatestake our code assessment.
We do a SQL test on CodeSignalto review candidates skills.
I really enjoy having something,like, technically grounded, where I'm
able to see the code you can write.
(10:29):
It doesn't really work well to do, like,a quick Tableau assessment, but SQL's
such a core skill, and it's really easyto test with a lot of SQL questions.
We're doing some grouping.
I think.
There might be a windowfunction question on there.
So at that stage, actually everybodypassed our SQL interview, but we
did have one candidate accept adifferent offer at that stage and exit.
(10:49):
Yeah.
Our average completion timeon that stage was 24 minutes.
My goal is also to keepthat stage pretty short.
I don't want to ask youfor like a six hour test.
You're applying for lots of jobs,especially at the entry level.
I'm not trying to keepyou for many, many hours.
The stage actually that we movedto was my hiring manager interview.
And in that stage, I'm asking usuallysome more problem solving questions.
(11:11):
So I'm going to ask you about somethingin your portfolio, something that you've
gone deep on, and ask you things like,how would you expand that project?
What else are you curious aboutthis project that you might've
worked on in your portfolio?
If you were rebuilding it, like, whatwould you do differently this time?
What other data would behelpful for driving decisions?
Those sorts of questions to dive deep.
Again, like, I'm not asking you aboutall of your experience in data analytics.
(11:34):
I assume that you don't have thatapplying for an entry level role.
I talked to six in the higher edgerstate and four of them went through.
The biggest gap for the two candidateswho exited there, I think was like
visualization and data exploration skills.
So then we moved into the teamtechnical interview where I have two
of the senior analysts on my team gothrough much more technical questions.
(11:55):
So in that stage, you're going tosee like, let's walk through your
portfolio project and talk about like.
How you build this in Tableau, you putsomething on Tableau public, we're going
to talk through the stages of building it.
So they're going to be vetting yourtechnical skills with a lot more detail.
This shouldn't be a scary stage.
Just feel confident speaking to thestages of not only how you did things,
(12:15):
like we're not going to ask whichbutton did you push, but think about
the methodology and why you choseto build something a certain way.
So like, if you chose to doa calculation in SQL versus
in a data visualization tool.
Why did you do that?
And how did you goabout figuring that out?
Those are going to be the sortsof questions to talk to there.
Avery (12:34):
Okay.
Awesome.
So then you're, you're analysts, you'rekind of doing this like team interview.
Now let me ask you this.
I mean, they've done this probably a fewtimes, maybe in their careers, right?
Do you give them questions?
Do they come up with their own questions?
Megan (12:47):
They come up with their own questions.
I talk to them primarily about the goals.
This is very similar to my managementstyle in general is I want to
talk to them about the goals.
What are we trying to find out?
To bring it all back into the data world,interviewing is a form of getting data.
This is a means of data collection.
So I talk to them about like,what do we want to learn about
the candidates at this stage?
And I will help them with writingquestions if they need it.
(13:08):
But for the most part, I'mtelling them like, I want to learn
about their technical skills.
I want to learn about how theygo about solving problems.
I want to learn about howcomfortable they feel in this system.
And you should be able tocome back and tell me about.
After that, that'sactually our last stage.
So we do that technical interview andthen I'm reviewing all of the feedback.
So our system.
As we collect scorecards afterevery interview, and then I have
(13:29):
access to review all of them.
So I can see something that's beenscored relatively objectively across
every interview and every candidateand sort of evaluate how that adds up.
So that'll be scoring onthings like technical skills.
How are your SQL skills?
How are your Tableau skills?
But it'll also include things likeproblem solving and other soft skills.
How are you as a communicator?
And I can evaluate against all of that.
(13:50):
In this case, I had two candidatesthat I was sort of debating
between in the final stage.
And then I made the call onwho to extend an offer to.
Really the differentiator forthe candidate who got the offer
is we're an education company.
We're here to help people upskill,learn data analytics, was that she
had prior experience in education.
So all of her technical skills were great.
Her communication skills were great.
(14:11):
Her portfolio was great, but I hadmultiple candidates who met all of that.
So her differentiator was reallythat education experience that
was really helpful for us.
It was something that sether apart and made her like
the perfect candidate for us.
Avery (14:25):
And I want to emphasize that here.
I work with a lot of teachers whowant to get into data analytics
and a lot of them are fearful.
Hey, I don't have a technical background.
I come from an unusual background,but in this case, that non technical
background, the unusual backgroundwas actually kind of the superpower
that got her the job or him the job.
Megan (14:42):
Yeah, like it's super, super helpful.
I can combine that portfolio,combine all the things that you've
learned about data analytics withthe other things that you know.
Somebody out there is makinged tech software that needs
to be sold to teachers.
Like you understand teaching, youunderstand the education world.
You can apply that knowledge todata analytics in that setting.
Be the perfect candidate for thatcompany rather than a pretty good
(15:04):
candidate for a whole sea of companies.
And the same could be applied again forlike, if you've got retail experience
or customer service experience, youmight look at a customer service
analytics role, which there are plenty.
Take your prior experience in customerservice and apply that to analytics.
I did it myself.
Like that's how I got intoanalytics was I studied healthcare
in my undergraduate program.
(15:25):
And I took an analytics roleat a healthcare company.
So when you can sort of combinethose things, it makes a much
more powerful profile, makesyou a much stronger candidate.
Again, like you don't haveto be okay for everybody.
Okay.
For everybody will get you a lot of likelooks, but you'll get the offer more when
you can find a way to make yourself likejust right for one company, those things.
Avery (15:46):
Okay.
How awesome was that behind the scenes?
I'm going to it's like to actuallyhire someone in the data space.
285 applicants just erased half ofthem because they didn't know Tableau.
That's why it's so important tolist all data skills pretty much
on your resume and your LinkedIn.
12 spoke to a recruiter andout of those 12, only 7 made
it to the next stage, which wasactually a SQL little coding test.
(16:09):
Everyone pretty muchpassed the coding test.
One person got offered ajob and so dropped out.
So six people talked to thehiring manager, which was Megan
in this case, kind of a behavioralinterview like you heard.
Out of those six, she kicked twoout after that and finally had
four interview with her team.
The team was part of the process,which I think is really neat.
And it goes back to what Alex wastalking about earlier, how you
(16:30):
really do need to mesh with the team.
Well, out of those four, two of themkind of stood out, but that couldn't
really choose between the two.
And the benefit of the doubt went tothe person whose domain experience,
like their past experience that wasn'tdata related would help the team.
In this case, it was someonewith an education background, and
this was an education company.
And so that's who they endedup hiring, which is awesome.
(16:51):
And I think for you, you shouldreally be thinking about.
You know, how can I use mydomain and my previous experience
to help me land my next job?
Hopefully your hiring manager is asgood and as kind as Megan, because I
think she hired very well in this case.
The next hiring manager we aregoing to hear from is Jesse Morris.
And I want you to pay attention towhat he thinks is most important
(17:12):
in the hiring process, because onceagain, I don't think you're going
to be able to guess what it is.
When you've hired some of those entrylevel people, what stood out the most
to you in those hiring processes?
Jesse (17:21):
Yeah, that's a great question.
And I think, you know, if you takeanything from today's conversation,
I think it's around this.
And, you know, again,I think it gets lost.
You've got to be the most technical in theroom or, you know, your ability to build
a dashboard and make it a work of art.
You know, that's like the most important.
I actually don't think that's the case.
And I actually think, Avery, you andI talked about this about, like, how
a lot of teachers make great analysts.
(17:43):
And I think there's a lot of truthto that because ultimately when I
think when it boils down to it Really?
It's it's a couple of key things oneIt's the ability to tell stories and be
succinct and that is not that's not justa data skill set That's a life skill set
You know If you look actually my originalbackground is in sales like I actually
I should say my second job My firstjob was I was a data analyst and then
(18:05):
I realized I needed to get presentationskills and the ability to tell stories
So I went into sales for a few years.
And so I think, you know, that skillset,the ability, but I think you can get
that in a myriad of ways, you canbe a great writer, you can, there's,
there's so many different ways youcan get that skillset, but I think
that's such a big one, especially.
You know, a lot of my time is spentcommunicating to executives and
to leadership teams and to boards.
(18:26):
Like I spent a lot of time tellingstories to the board and that's really
key is that ability to kind of boilthings down and to here's the most
important and then you can work back.
Ultimately, like people, when they getcurious about data, that's when they
start asking kind of your next layerof questions and you, you can make
that, you can bring that curiosityof the life through storytelling.
The other one, which isprobably a little bit.
(18:46):
Less common you want here, but this issomething that just continues to even
today even with senior analysts Itdoesn't matter what level of analysts
you are but tenacity and mentaltoughness Wow, so that's a really funny
one I tenacity to me like in my world.
I work in these smaller calledstartup type Uh, technology companies.
And so we're moving at reallyfast pace, but we don't get
(19:10):
weeks to work on projects.
So if you work in any large corporatecompanies, you're going to get that.
And that's okay.
I think ultimately it's goodto know, like, what are, what
type of environment you're in?
And so if you don't work necessarilywell under pressure and some of these
things I'm about to talk about, that'sokay, then you're probably maybe better
designed to work at larger companieswhere you're given the freedom to like,
(19:30):
sit down and work on things for weeks.
The environment I work in,we're not given that time.
And so the ability to, you know,change prioritization on a dime, to
juggle nine different projects at once.
If you talk to my data analysttoday, like this is the reality.
Like we, this week, we came into theweek with a plan and by Monday afternoon,
you know, it was Monday morning duringour standup and by Monday afternoon,
(19:51):
that plan got halfway derailed, right?
And so it's a reprioritizationgame and that's not for everybody.
I mean, I think ultimately.
You know, that's a tough thing to wrapyour head around and not get frustrated.
And, and I think you and I talked aboutthis before, but it's that like knowledge
of, I understand what perfect looks likeor really phenomenal looks like, but
I also understand what good enough is.
(20:12):
And I think that skill set,that's a really important one.
And that's not like, you know, I'm goingto learn this by watching X, Y, and Z.
I think that's something that youactually have to work towards and
build up that, that mental toughness.
I actually think failure, you know,it's easy to look at a resume and be
like, Oh, all this stuff went great.
I was a founder, youknow, at a tech company.
Good for me.
I also failed at that tech company.
Right.
I learned a lot of things throughtrial and error and that I think
(20:35):
it's the same for all of us.
And so those would be some of thethings I think that really stand out
to me when you boil it down to likesome of the key pieces behind it.
It's an attitude, right?
Like it's that willingness to say,Hey, I messed up here and that's okay.
Like, cool.
What'd you learn from it?
How do we make it better?
How can I help?
But I think, you know, thoseare ultimately some of the, when
you boil it down to some of thethings that I look for, no matter
(20:57):
what stage you're at within it.
And then I think.
You know, on the specifically on thestarting out analyst in particular, you
know, I think just a, again, perspectiveis an interesting one, but did you have
a sales background or did you work for,I mean, maybe you're working in retail.
Did you work for Banana Republicduring college where you were
like, all of those things,perspective and data is everything.
(21:18):
And what I mean by that is like yourability to speak into it from the
person who's asking the questionor the departments or the leaders
that are asking the questions.
Right.
Because as long as you've got justthat various perspective, that
actually has a lot more value.
I think sometimes the technical even does.
Avery (21:32):
Yeah.
And I hope people just heard whatyou said, because I think that's
very impactful, you know, justto kind of rehash them a bit.
It's not necessarily how technicalyou are that lands you the job.
Because I think you said thisphrase when we first originally
talked that the technical stuff.
It's kind of expected.
That's like you, you haveit or you kind of don't.
Right.
And it's really your storytelling,your grit, your attitude that separates
(21:55):
you, which I think for all of youguys listening who want to be aspiring
data analysts, that should be reallyrewarding because you can have grit.
You can be, you know,you can be authentic.
You can try hard.
You can have passion.
You can become a good,you know, storyteller.
Those aren't like necessary, like youhave to be spending 25 years of your life
in SQL to know how to master everything.
(22:16):
Right.
That's really, in my opinion, enlighteningand refreshing to hear because it can be
like, I think most people take the datacareer job hunt way too skill heavy.
Of course, skills are important.
Right.
But like, they're not everything.
And I think you kind of just said thatbasically, they're not everything.
All right.
Hopefully you're catchingthe drift at this point.
It was a pretty similar themehere that your technical skills,
(22:37):
of course, they're important.
It's the bare minimum.
It's actually things like yourstorytelling, the ability to be
succinct that sets you apart.
And that's something you'reprobably thinking, Avery,
you're not very good at that.
I've listened to your podcast episodes.
I've watched your YouTube videosand you're not very succinct.
And it's true.
It's something I'm still working on.
And I think it's a lifelong journey, butonce again, this is like Jesse said, a
(22:59):
life skill, not necessarily a data skill.
And so that's something that we canpractice and we can get better at.
And no matter how technicalor how non technical you are.
It's something that you canimprove on every day and work at.
Jesse also brought up tenacityand mental toughness, and this
is something that we can all do.
One thing that I do is I actuallytake ice cold showers and baths to
try to increase my mental toughness.
(23:20):
Kind of weird, but it works for me.
The last hiring manager we aregoing to hear from is Andrew Madsen.
I want you to pay attention to whathe says because he kind of repeats
what has already been said, but headds one really important point.
Part, uh, that the other oneshaven't talked about as much,
and that is projects, which isthe P part of the SBN method.
Let's take a listen.
I wanted you to walk us through theidea of when you're hiring a data
(23:42):
analyst, you know, what's reallyimportant to stand out as a candidate.
What can these listeners do to To standout and the data analyst job search.
Andrew (23:51):
Yeah.
My thoughts on thishave evolved over time.
So the data analyst position hasjust grown and grown and grown as
our needs for quality data analystshave really permeated every industry.
So there's a lot of opportunitythere before when I was new at
hiring data analysts and I wasnew into data myself, I really was
focused on the technical skills.
(24:11):
I was looking at whatever my stack was,like we use Tableau, whatever it is.
And I was looking for applicantswho match that data stack.
That's how I began looking for applicants.
Now what I look for if I was hiring a dataanalyst, I focus much more on the person.
I look for somebody who's curious.
I look for somebody who's resilient.
(24:33):
I look for somebody who's goingto mesh well with the team because
data analytics is a team sport.
You know, one person who just isn'ta team player can really throw off
the whole dynamic and ultimatelythe work and the business insights
that we're trying to drive.
So less important to me is yourspecific technical skill set.
You know, if you know Tableaureally well and we're using Power
(24:54):
BI, that's totally fine with me.
But you're demonstratingthat ability to learn.
And some of the ways that youcan do that, like Avery always
talks about, are projects.
I love to look at projects.
I love to see interesting projects.
We've all seen the Titanic dataset,and I don't mind if you use that, but
I want to see something that you'reinterested and passionate about, and
(25:14):
I want to learn about that with you.
And then if we're interviewing, Ireally want you to tell that story,
because the ability to communicateas a data analyst is so important.
You know, I don't want to haveto go to the stakeholders and
explain what you were doing.
I want you to go and representyourself and present your insights
and build those relationships.
So if you can have something you'repassionate about, you uncovered some
(25:34):
insights and you can communicate those ina story and a narrative that's engaging.
Those are so important.
Those will really set you apart.
Avery (25:42):
Okay.
That's awesome.
A lot to unpack there.
I think we, as data analysts,candidates often over index.
On how much, you know, thetechnical skills matter and
the technical skills do matter.
There's people who are willingto take a chance on you and you
have to show them that you're morethan just, you know, some NPC.
I don't even know what thateven, what that means right now.
(26:04):
What does that mean?
A non role playing, I don't even knowwhat it means, but it's like a non player
Andrew (26:08):
character.
Yeah.
Avery (26:09):
Non player character.
You have to show some sort of passion,some sort of personality, some sort of
drive, some sort of like, And that canbe even your grit, your communication,
you know, what you like about projects.
And it's just interesting that we'vehad a couple of different data hiring
managers on the podcast now, and they'veall let off with a very similar message.
There you have it, folks, advicestraight from hiring managers on
(26:30):
how to land your next day at a job.
If you want even more On how toland a data job, I highly suggest
checking out my newsletter.
Every week I send you a tip that helps youtake the next step in your data career.
You can subscribe at datacareerjumpstart.
com or in the show notes down below.
And if you want even more help onyour data journey, consider joining
the data analytics accelerator,which is my ten week bootcamp to
(26:52):
help you land your first data job.
You can find that link inthe show notes down below.
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