Microsoft Data Analyst Case Interview – Product Telemetry, SQL, and Experimentation for Microsoft Teams

This 60-minute case mirrors Microsoft’s product analytics interviews and emphasizes customer obsession, structured problem solving, and clear storytelling. You’ll act as a Data Analyst embedded in the Microsoft Teams Growth & Engagement group. Prompt: “Usage of a new Meeting Recap feature has plateaued after launch. Define success, diagnose adoption, and recommend next steps.” Expect to: (1) Clarify scope and constraints (tenants vs. consumers, device mix, geo, privacy/PII considerations, Responsible AI). (2) Propose a metric framework: North Star (e.g., weekly active meeting recap viewers per tenant), supporting metrics (feature adoption, activation funnel, 7/28-day retention, user and tenant-level engagement), and guardrails (crash rate, latency p95, email/send failures, support tickets). (3) Design an experiment/flight: propose treatment/control, eligibility, unit of randomization (user vs. tenant), success metrics, minimal detectable effect, power and duration, and plan for ring-based rollout. (4) Write and explain SQL for a simplified telemetry schema the interviewer provides (tables: events, users, tenants, experiment_assignments). Expect to use joins, CTEs, window functions for retention/quantiles, and to reason about missing/late events. (5) Diagnose adoption using event data (e.g., create a funnel from “meeting end” → “recap surfaced” → “recap opened” → “tasks created”); segment by tenant size, license (E3/E5), platform (desktop/iOS/Android), and cohort (new vs. existing feature users). (6) Recommend actions: hypotheses (e.g., poor discoverability in mobile, slow load times for large tenants), targeted experiments (UI placement, notification timing, first-run education), and success criteria tied to business outcomes (retained active usage and reduced time-to-value). (7) Communicate trade-offs and next steps in a concise exec-ready narrative aligned to Microsoft’s culture (growth mindset, One Microsoft, inclusion). Evaluation rubric: clarity of problem framing and assumptions; metric rigor and guardrail coverage; practical experimentation design; correctness and efficiency of SQL; ability to reason with imperfect telemetry and data quality checks; crisp, customer-focused storytelling and stakeholder alignment.