Lead Data Engineer
Posted Oct 5, 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 optimized a slow pipeline. What did you find and fix?
What they are checking: Whether they diagnose slowness with evidence like query plans and metrics rather than guessing at fixes.
How do you mentor engineers and review their code without slowing the team down?
What they are checking: Technical leadership that raises quality while keeping delivery moving.
What would you do
A nightly pipeline that feeds claims reporting failed and leadership needs the numbers this morning. What do you do?
What they are checking: Incident handling, communication and root-cause follow-up under pressure.
A business team wants a new data source available next week, but you see quality problems. How do you handle it?
What they are checking: Ability to push back with options and protect data quality while meeting business needs.
About the work
Walk me through a large-scale data pipeline you designed. What were the volumes, tools and tradeoffs?
What they are checking: Real experience building high-volume pipelines and the reasoning behind design choices.
How do you build data quality checks into a pipeline so bad data doesn't reach reports?
What they are checking: Practical validation strategies, including where checks run and what happens when they fail.
What's your experience with real-time pipelines, like Kafka or Spark Streaming, and when do you choose streaming over batch?
What they are checking: Judgment about when real-time processing is worth its complexity.
How do you standardize data management practices across a team of engineers?
What they are checking: Ability to set standards for staging, ingestion and provisioning that the team actually follows.
How do you work with DevSecOps on CI/CD for data pipelines?
What they are checking: Understanding of testing, deployment automation and security in data engineering workflows.
How do you handle data destruction and retention for sensitive health data?
What they are checking: Awareness of data lifecycle rules and HIPAA concerns, which the posting names directly.
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As published by Centene.