Senior BI Engineer (Hybrid)
Posted Sep 2, 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
Describe a project where you owned the full lifecycle - from a stakeholder's vague ask through scoping, building, and supporting it after launch. Where did it almost go wrong.
What they are checking: end-to-end ownership and honesty about project pitfalls, not just success stories
Tell me about a time you had to explain a technical limitation or tradeoff to a senior business leader who wasn't technical. How did you frame it.
What they are checking: ability to translate technical reality into business language for senior audiences
Describe a time you inherited or found a data pipeline or report that was technically working but poorly built. What did you do about it.
What they are checking: willingness to improve fragile systems and advocate for best practices, not just ship features
What would you do
Say you're building an ETL pipeline in Astronomer Airflow and a DAG starts failing intermittently in production, only during peak load. How would you go about diagnosing and fixing it.
What they are checking: practical orchestration debugging skills under real-world pipeline pressure
A Finance stakeholder and a Marketing stakeholder both want changes to the same dashboard, and their requirements conflict. How do you handle that conversation.
What they are checking: stakeholder management skill and comfort navigating competing priorities across business units
If you were asked to find new AI use cases across a business unit you didn't know well, how would you go about identifying where AI could actually help versus where it would just be noise.
What they are checking: judgment and initiative in driving AI adoption rather than chasing hype
You're given a request from a business owner that's really a symptom of a deeper data problem, not the actual fix they need. How do you handle that conversation without just doing what they literally asked.
What they are checking: requirements-gathering maturity and pushing back constructively on stakeholders
About the work
Walk me through a SQL query you wrote that was genuinely complex - what made it hard, and how did you make sure it stayed performant as data volume grew.
What they are checking: depth of real expert-level SQL experience versus surface familiarity
Tell me about a time you had to design a data mart or warehouse structure from scratch. How did you decide between a star schema and other approaches, and how did you handle slowly changing dimensions.
What they are checking: actual hands-on dimensional modeling knowledge, not textbook recall
What's an AI tool you've actually used in your day-to-day BI or data work, like a coding assistant or something built on Claude, Gemini, or Bedrock, and what did it change about how you worked.
What they are checking: genuine hands-on AI tool adoption versus buzzword familiarity
Have you worked with MicroStrategy or a comparable enterprise BI tool like Tableau or Looker? Tell me about a dashboard or report you built that stakeholders actually relied on daily.
What they are checking: real depth with enterprise-grade BI tools versus lightweight reporting experience
Tell me about a time your Python skills mattered for a data transformation or pipeline task that SQL alone couldn't handle well.
What they are checking: whether Python is a genuine working skill or just listed as nice-to-have
How have you worked with Snowflake or a comparable cloud warehouse like Databricks or Redshift at scale - what kind of performance or cost issues have you had to solve there.
What they are checking: specific hands-on cloud data warehouse experience at meaningful scale
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As published by Enova.