Datadog Data Analyst Case Interview: Observability Analytics and Root-Cause Investigation

This Datadog-style case simulates a real incident workflow: you’ll investigate a sudden spike in p95 API latency and error rate in a cloud service, using simplified "metrics" and "logs" tables plus a brief release timeline. Your goal is to quantify impact, segment the issue (service, endpoint, env, version), form hypotheses about root cause (e.g., recent deploy, dependency degradation), and communicate next steps as if partnering with product and engineering. Expect to reason from time-series/observability artifacts and narrate an incident-style debrief. ([glassdoor.com](https://www.glassdoor.com/Interview/Datadog-Interview-Questions-EI_IE762009.0%2C7_IN86.htm?utm_source=chatgpt.com)) Format and timing (70 minutes total): - 5 min: Clarify context, success metrics (SLO/SLA), and constraints. - 30 min: Live SQL analysis (Postgres-like). Tasks typically include joins, window functions for percentiles (p95), time-bucketing, anomaly comparison vs. baseline, and handling missing/late events. Interviewers often use a guided, collaborative pad with progressively harder prompts. ([glassdoor.sg](https://www.glassdoor.sg/Interview/Datadog-Data-Analyst-Interview-Questions-EI_IE762009.0%2C7_KO8%2C20.htm?utm_source=chatgpt.com), [datalemur.com](https://datalemur.com/blog/datadog-sql-interview-questions?utm_source=chatgpt.com), [interviewquery.com](https://www.interviewquery.com/interview-guides/datadog-data-analyst?utm_source=chatgpt.com)) - 15 min: Experiment/product analytics vignette. Propose a way to validate the fix (A/B or phased rollout with guardrails), define success/health metrics (latency, error rate, throughput), and outline dashboards you’d build for stakeholders. - 15 min: Communication and recommendations. Present findings to a mixed audience, call out trade-offs, risks, and data quality issues, and propose immediate/longer-term actions and owners—reflecting Datadog’s collaborative, pragmatic, iterative culture. ([careers.datadoghq.com](https://careers.datadoghq.com/detail/5778932/?utm_source=chatgpt.com)) - 5 min: Q&A and follow-ups (what you’d monitor next, additional data you’d pull, how you’d validate). What interviewers look for: - Analytical rigor and SQL fluency tied to observability data (metrics, logs, traces); ability to segment and quantify impact quickly. ([glassdoor.sg](https://www.glassdoor.sg/Interview/Datadog-Data-Analyst-Interview-Questions-EI_IE762009.0%2C7_KO8%2C20.htm?utm_source=chatgpt.com)) - Clear, transparent communication, humility, and customer-focused judgment under time pressure; strong cross-functional collaboration signals. ([careers.datadoghq.com](https://careers.datadoghq.com/detail/5778932/?utm_source=chatgpt.com)) - Structured problem solving and comfort with ambiguity; many loops include a values/experience round where you narrate a project and field follow-ups. ([reddit.com](https://www.reddit.com/r/leetcode/comments/1g6183s?utm_source=chatgpt.com), [interviewing.io](https://interviewing.io/datadog-interview-questions?utm_source=chatgpt.com)) Materials provided: short incident brief, two small tables (e.g., metrics_hourly, http_error_logs) with timestamps, service/endpoint, env, version, response_time_ms, and status_code; optional small "deploys" table with service, version, deployed_at. Tools: SQL in a shared editor; whiteboard/verbal visuals for charts; no heavy notebook required.