
Atlassian Data Analyst Case Interview — Product Metrics, Experimentation, and Stakeholder Decisioning
This Atlassian-style case simulates diagnosing and improving a key product metric across Jira and Confluence for our 250,000+ customer base. You’ll be given a brief on a recent feature rollout (e.g., a new Jira issue-create flow) and a lightweight event schema (page_view, issue_created, editor_loaded, invite_sent, invite_accepted, purchase_started, purchase_converted) with sample columns (user_id, site_id, plan_tier, region, timestamp, client, experiment_bucket). The interview assesses: 1) Problem framing and metric design — define a north-star and a small set of supporting metrics (e.g., activation, team adoption, D7 retention, collaboration actions like mentions/shares), articulate trade-offs, and propose guardrail metrics (latency, error rates); 2) Analytical approach — outline SQL you would write to compute activation and retention by site_id (tenant-level bucketing is important for team tools), segment by plan_tier and region, and investigate a 3–5% MAU dip in EMEA after the rollout; discuss sampling, sessionization, and basic QC checks (event drop-offs, late-arriving data, bot filters); 3) Experimentation and causality — propose an A/B or phased rollout design with site-level randomization, minimal detectable effect assumptions, power trade-offs, and methods to control seasonality and enterprise freeze periods; identify and handle instrumentation gaps, multi-product interference (Jira ↔ Confluence), and backwards compatibility of the event taxonomy; 4) Product sense for collaboration tools — propose hypotheses specific to Atlassian (e.g., friction in issue creation for new teams, permission or project defaults, onboarding flows, invite loops), and suggest minimally invasive product changes and success criteria; 5) Communication and culture — work “in the open” by narrating assumptions, structuring a concise recommendation using the DACI decision framework, and drafting a one-page Confluence summary with a simple chart/table and next steps. The session is collaborative and interviewer-led with time-boxed prompts; whiteboarding or a scratchpad is encouraged, but code need not run. Expect follow-ups on trade-offs (why site vs. user bucketing), how you’d productionize a Looker/Amplitude dashboard, and how you’d partner with PM/Design/Engineering to ship and validate changes while upholding Atlassian values like “Don’t #@!% the customer,” “Open company, no BS,” and “Build with heart and balance.” Deliverables by the end: a crisp problem statement, prioritized analyses you’d run with illustrative SQL snippets, an experiment plan with guardrails, and a DACI-style recommendation with measurable outcomes and owners.
8 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