Databricks Data Analyst (Engineering) — Behavioral Interview Focused on Customer Obsession, Ownership, and Data Storytelling

This behavioral interview for Databricks Data Analyst roles in Engineering evaluates how you drive impact in a fast-moving, data-first environment aligned to the Lakehouse mission. Expect a structured conversation (typically via a GoodTime-scheduled video call) that probes deeply into real projects you owned end-to-end. Focus areas: 1) Customer obsession: partnering with internal customers (e.g., Product, Sales Ops, Marketing, Finance) to clarify ambiguous asks, define the right success metrics, and say no or renegotiate scope when needed. 2) Ownership and bias for action: unblocking yourself, moving from vague problem to measurable outcome, handling trade-offs under time pressure, and following through without hand-holding. 3) Data rigor: letting data decide; designing trustworthy metrics/experiments; ensuring data quality and reproducibility (e.g., clear lineage, validation, incident postmortems); communicating risk and uncertainty. 4) Cross-functional collaboration: influencing without authority, resolving conflict with data engineers/PMs, and crisp written/spoken communication tailored to execs vs. ICs. 5) Learning mindset: reflecting on misses, feedback you sought, and how you raised the bar for the team. Format: 5–10 min intro; 30–40 min STAR-driven deep dives with persistent follow-ups (e.g., exact role, constraints, metrics, and results); 5–10 min for your questions. Come ready with 4–5 quantifiable stories covering metric design and adoption, stakeholder pushback, debugging a data issue in production, delivering under a hard deadline, and a time you influenced strategy with insights.