Data Analyst interview
at Stripe.

Tell-me-about-a-time stories on ownership, conflict, and impact — scored against this company's real values.

1. 01 Tell me about a time your analysis surfaced an uncomfortable truth that a stakeholder didn't want to hear, and how you handled it.
2. 02 Describe a time you took ownership of a messy or untrustworthy dataset and made it reliable for decision-making.
3. 03 Walk me through how you communicated a complex payments finding to a non-technical audience to drive a decision.
4. 04 Tell me about a time you caught an error in your own analysis before it shipped and what rigor practice helped you find it.

The hard screen for the craft itself — talked through out loud, not whiteboarded in your head.

1. 01 Write a SQL query using window functions to find each merchant's first successful payment and their time-to-second-payment.
2. 02 Explain the difference between ROW_NUMBER(), RANK(), and DENSE_RANK() and give a payments scenario where choosing wrong would corrupt a metric.
3. 03 A daily revenue dashboard suddenly drops; walk me through how you'd query and isolate whether it's data, product, or real.
4. 04 Given charges and refunds tables, write SQL to compute net revenue and dispute rate per merchant per month, handling partial refunds correctly.

Open-ended design of the systems this company actually runs, with the interviewer probing your tradeoffs.

1. 01 Design a set of metrics and a dashboard to monitor merchant authorization-rate health across regions and card networks.
2. 02 Design a reconciliation check that verifies reported revenue matches the underlying ledger and flags discrepancies.
3. 03 Design an A/B test analysis framework for measuring the impact of a checkout change on conversion with guardrail metrics.
4. 04 Design a merchant-lifecycle funnel model from signup to first payment to retention, and the data needed to populate it.