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Google Data Analyst Case Interview: Product Metrics, Experiment Design, and SQL Problem-Solving

This Google case interview simulates diagnosing and measuring the impact of a feature change on a core Google product (e.g., Search, YouTube, Ads, or Maps). You’ll be asked to: (1) translate an ambiguous product goal into a clear problem statement and define success with a hierarchy of metrics (north-star, input/behavioral, guardrails such as latency, reliability, policy compliance, and revenue where applicable); (2) propose an experiment/measurement plan—formulate a hypothesis, choose the unit of randomization, outline a high-level power check and duration estimate, call out seasonality and novelty effects, specify guardrails, and discuss rollout/ramp considerations at Google scale; (3) debug a sudden metric drop using a structured diagnostic tree (instrumentation checks, cohort/geo/device slices, event pipeline integrity, spam/abuse filtering, launch calendar cross-references); (4) work through a short SQL-style prompt to compute core KPIs (e.g., CTR or conversion funnel), deduplicate events, use window functions for cohorts/retention, and reason about missing or delayed data; and (5) communicate insights with crisp narratives and visuals, culminating in a recommendation and next steps. Interviewers emphasize Google’s culture: user focus, rigor at massive scale, data integrity/privacy, collaboration with PM/Eng, and “Googleyness” (humility, clarity, bias to action). Format typically includes quick scoping questions, a whiteboard/shared-doc walkthrough of your approach, lightweight SQL/metrics reasoning, and a brief retrospective on trade-offs and risks.

engineering

60 minutes

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About This Interview

Interview Type

PRODUCT SENSE

Difficulty Level

4/5

Interview Tips

• Research the company thoroughly

• Practice common questions

• Prepare your STAR method responses

• Dress appropriately for the role