In this episode, Chris Harbert sits down with Jitendra Boddapati, a lead quality engineer with over 10 years of experience specializing in API testing, ETL, big data QA, and accessibility testing across healthcare, banking, and retail domains.
Jitendra shares his journey from college graduate to leading complex ETL projects, including how he taught himself SQL under pressure to deliver a challenging data migration project. The conversation dives deep into the often-overlooked world of ETL (Extract, Transform, Load) testing—what it is, why it matters, and how it differs fundamentally from traditional UI testing.
Key topics covered:
Why ETL testing doesn't get the attention it deserves and how to change that
The critical role of Source to Target Mapping documents for ETL testers
Real-world production bugs: How a comma in a provider name caused data to shift into the wrong columns
Data profiling techniques like pattern and frequency analysis to uncover hidden anomalies
Using AI tools to generate SQL queries and automate data validation
Chris and Jitendra also explore how AI is transforming user interfaces and what that means for testers. When users interact with your product through Claude, ChatGPT, or Cursor instead of your website, who's responsible for testing that experience?
The episode wraps up with practical advice for anyone looking to get started with ETL testing: understand your data scope, break pipelines into smaller chunks, and always start with clear requirements.
Whether you're a seasoned data engineer or a developer curious about testing data pipelines, this episode offers valuable insights into a testing discipline that's becoming increasingly critical as organizations deal with ever-growing volumes of data.
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