Case study
Data Testing & Defect Validation for Production Pipelines
Designed and executed validation workflows to ensure data fixes actually worked and did not introduce regressions into production pipelines.
Context
Data pipelines were being actively developed and patched as defects were discovered. However, fixes often caused side effects that were only detected after deployment.
The problem
- No consistent way to verify whether a fix actually resolved a defect
- Regression bugs were common after changes
- Business teams lacked confidence in reported numbers
What I built
A structured validation workflow that converted defect reports into testable data checks and ensured fixes were verified before and after deployment.
- Test cases derived from real defect tickets
- Before/after dataset comparisons to confirm fixes
- Regression checks on impacted downstream tables
- Validation logs shared with engineering and QA teams
Impact
- Fewer production regressions
- Higher confidence in data fixes
- Better collaboration between QA, engineering, and analytics teams
Why this matters in interviews
- Shows you treat data like software (testable, verifiable)
- Demonstrates production mindset
- Highlights cross-team debugging skills