Databricks Data Analyst Case Interview — Lakehouse Product Analytics (Engineering Track)

What this covers: A 60-minute, business-first analytics case delivered in a Databricks environment (notebook or Databricks SQL). The case simulates a real stakeholder ask from a PM or GTM leader and assesses how you turn messy event and transaction data in the Lakehouse into clear, defensible insights and recommendations. Format and flow: - 5 min: Problem framing with a Databricks interviewer (collaborative; clarifying questions encouraged). - 35–40 min: Hands-on analysis in a shared Databricks notebook or SQL editor working with Delta tables (e.g., events, sessions, orders, customers). You’ll write Spark SQL (optionally some Python) to explore data, build metrics, and diagnose patterns. Expect to reason about late/duplicate events, schema drift, and nulls. - 10–15 min: Readout. You’ll synthesize findings into a concise, exec-ready narrative and propose next steps, tradeoffs, and potential experiments. Representative prompt (example): “Our self-serve funnel conversion dipped ~8% WoW. Using the Lakehouse tables provided (events, sessions, plans, orders), identify what changed, quantify impact by segment (region, plan, device), and recommend actions. If you suspect an instrumentation issue, show how you’d validate and estimate the true effect.” Technical focus areas specific to Databricks: - Spark SQL proficiency: window functions, CTEs, conditional aggregates, semi/anti-joins, approximate distinct, sessionization basics. - Lakehouse fundamentals: reading/writing Delta Lake tables; ACID and time travel for before/after comparisons; handling schema evolution; understanding partitioning and file size considerations at query time. - Performance and cost-aware thinking: when to leverage caching, Z-ORDER/OPTIMIZE for selective filters, and when a Databricks SQL Warehouse vs. all-purpose cluster is appropriate; basics of Photon benefits at a high level (no deep internals expected for this role). - Data quality: detecting duplicates/late-arriving data; idempotent calculations; event taxonomy sanity checks; guardrails for metric definitions. - Governance: awareness of Unity Catalog concepts (permissions, lineage at a high level) in the context of reproducible analysis and stakeholder trust. Business analytics depth: - Metric design and interpretation: DAU/WAU/MAU, activation and retention cohorts, conversion funnels with drop-off analysis, LTV/CAC reasoning at a directional level. - Experimentation intuition: success metrics, guardrail metrics, sample sizing tradeoffs (conceptual), segmentation pitfalls, novelty and seasonality checks. - Storytelling: move from exploration to a crisp recommendation, enumerate risks/assumptions, and propose a testable plan with estimated impact. Deliverables expected during the interview: - Clean, readable queries (or notebook cells) with brief commentary explaining approach and assumptions. - 2–3 core charts or pivoted tables (e.g., funnel by segment, retention curve, WoW deltas) using Databricks SQL visualizations or quick Python plotting if preferred. - A short verbal exec summary: what happened, why it matters, what to do next, and what you’d monitor. Evaluation rubric aligned to Databricks’ interview style: - Technical correctness and rigor (Spark SQL, Lakehouse fluency, data quality handling) — 40% - Product sense and business reasoning (impact-focused, practical tradeoffs) — 30% - Communication and collaboration (clarifying questions, narrative clarity, stakeholder empathy) — 20% - Ownership and bias for impact (proactive next steps, risks, and follow-ups) — 10% Tips reflective of real Databricks interviews: - Ask for metric definitions up front; confirm event semantics (e.g., what uniquely defines a session or signup) to avoid wrong conclusions. - Use time travel or clear before/after filters to isolate changes; call out data caveats explicitly. - Favor simple, explainable queries first; then add performance tweaks if time permits. - Tie every chart/table to a decision: what would a PM or Sales leader do differently based on this? What’s not required: deep Spark internals, complex ML pipelines, or production-grade ETL. Breadth across Lakehouse basics, strong SQL, and crisp business storytelling matter most.