Data Quality and Observability at Tekmetric with Ipsa Trivedi

Airflow’s adaptability is driving Tekmetric’s ability to unify complex data workflows, deliver accurate insights and support both internal operations and customer-facing services — all within a rapidly growing startup environment.

In this episode, Ipsa Trivedi, Lead Data Engineer at Tekmetric, shares how her team is standardizing pipelines while supporting unique customer needs. She explains how Airflow enables end-to-end data services, simplifies orchestration across varied sources and supports scalable customization. Ipsa also highlights early wins with Airflow, its intuitive UI and the team's roadmap toward data quality, observability and a future self-serve data platform.

Key Takeaways:

(02:26) Powering auto shops nationwide with a unified platform.

(05:17) A new data team was formed to centralize and scale insights.

(07:23) Flexible, open source and made to fit — Airflow wins.

(10:42) Pipelines handle anything from email to AWS.

(12:15) Custom DAGs fit every team’s unique needs.

(17:01) Data quality checks are built into the plan.

(18:17) Self-serve data mesh is the end goal.

(19:59) Airflow now fits so well, there's nothing left on the wishlist.

Resources Mentioned:

Ipsa Trivedi

https://www.linkedin.com/in/ipsatrivedi/

Tekmetric | LinkedIn

https://www.linkedin.com/company/tekmetric/

Tekmetric | Website

https://www.tekmetric.com/

Apache Airflow

https://airflow.apache.org/

AWS RDS

https://aws.amazon.com/free/database/?trk=fc551e06-56b0-418c-9ddd-5c9dba18569b&sc_channel=ps&ef_id=CjwKCAjwzMi_BhACEiwAX4YZULS4jV2Xpnpcac_Q3eS9BAg-klKUDyCt6XSdOul8BLHkmWzFFh4NXRoCGhQQAvD_BwE:G:s&s_kwcid=AL!4422!3!548989592596!e!!g!!amazon%20sql%20database!115430