QA Automation Engineer with Databricks
Posted Oct 1, 2026 · We last checked this listing on Oct 5, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about a data quality issue you caught that would have caused real business damage.
What they are checking: Impact awareness and the attention to detail data QA requires.
What would you do
A metric in a dashboard doesn't match finance's number. How do you find out which one is right?
What they are checking: Systematic tracing of business rules and aggregations back to source data.
Data engineers keep shipping changes without telling QA. How do you handle it?
What they are checking: Building collaboration and process so testing keeps pace without becoming a gatekeeper fight.
How do you decide which data validations to automate first?
What they are checking: Risk-based prioritization focused on the most business-critical tables and metrics.
About the work
How would you validate a source-to-target transformation in Databricks when the source has millions of rows?
What they are checking: Efficient reconciliation techniques like counts, checksums and sampling instead of row-by-row comparisons.
Walk me through how you'd test an incremental load that handles updates and deletes.
What they are checking: Understanding of change data handling and Delta table behavior, a common source of hidden data errors.
How do you test referential integrity across Delta tables that don't enforce foreign keys?
What they are checking: Writing SQL checks for orphan records and broken relationships in a lakehouse.
How do you integrate data tests into CI/CD so bad data doesn't reach production?
What they are checking: Practical pipeline integration and gating decisions for data quality.
How do you create test data for a pipeline when you can't use real healthcare data?
What they are checking: Building realistic synthetic data that still exercises edge cases safely.
A pipeline job fails halfway and retries. What would you test to make sure the retry didn't duplicate or lose data?
What they are checking: Thinking about idempotency and recovery scenarios, which the posting calls out directly.
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As published by Vizient.