Senior Data Engineer
Posted Sep 25, 2026 · We last checked this listing on Oct 6, 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 time you translated a vague data request into clear engineering requirements.
What they are checking: Whether they ask the right questions to understand the real business need before building.
Tell me about a time you set or influenced a best practice for a data team.
What they are checking: Whether they can lead through standards, as the posting asks them to define policies for data usage.
What would you do
A business user says the numbers on a dashboard you built don't match finance's report. How do you track it down?
What they are checking: Whether they trace definitions, filters and source timing systematically and fix the root cause.
How would you help grow self-service analytics without ending up with a mess of inconsistent reports?
What they are checking: Whether they think about curated data models, shared definitions and governance to make self-service safe.
Some reports will go to external customers. What extra care would you take?
What they are checking: Whether they think about data access controls, accuracy checks and confidentiality for outside audiences.
About the work
Walk me through a pipeline you built in Databricks with PySpark that lands data in Snowflake. What were the hard parts?
What they are checking: Real hands-on experience with the exact stack named, including how they handled scale and failures.
How do you design a pipeline so it can be rerun safely after a failure without duplicating data?
What they are checking: Whether they understand idempotent loads, merge logic and checkpointing.
How would you set up data quality checks on a pipeline feeding the Data Science team?
What they are checking: Whether they build automated tests for freshness, completeness and valid ranges, with alerting.
How do you tune a slow Spark job?
What they are checking: Practical knowledge of partitioning, shuffles, skew, caching and file sizes.
When would you model data as a star schema versus a wide flat table for reporting?
What they are checking: Whether they understand tradeoffs in usability, performance and maintenance for BI consumers.
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Practice this out loudThe full job description
As published by Conagra Brands.