Practice the SQL that payments and revenue analytics run on — MRR math, cohort retention, merchant and dispute analysis, window functions — on an ecommerce orders dataset and a synthetic card-transaction ledger, with an AI tutor when you get stuck.
35 challenges
Stripe-pattern set
Runnable data
Orders + a card ledger
AI tutor
Step-by-step hints
What this page is: we have no dated public source for how Stripe runs its SQL round, so this page does not state one — no duration, no platform, no stage list. What follows is general SQL interview practice on the kind of data Stripe works with. What is ours: the questions are SQL Quest challenges and the topic emphasis is our editorial judgement, not a measured breakdown of Stripe's interview. Pages with dated sources say so in this spot.
The 35 challenges tagged Stripe in the SQL Quest bank, with every raw challenge tag resolved to the 9 canonical skills. Each share is the portion of those 35 challenges that exercise the skill — a challenge exercises several, so the shares do not sum to 100%. This is the composition of the practice set on this page, not a measurement of Stripe’s interview.
These are SQL Quest challenges chosen because their SQL matches the work — Querying Basics, Aggregation & Grouping, Joins. They are not questions Stripe has asked, and this page does not claim to know its questions. All of the six play free; each card opens the challenge itself.
General guidance about analytics SQL work — not sourced from Stripe and not a description of its process. We have no dated, citable source for how Stripe runs its SQL round, so this page states none.
Skillmap
Ten questions, no signup. You get a readiness score weighted to the SQL this page covers, your Skillmap across joins, window functions, aggregation and the rest, and the weakest skill to practise first.
Drill the skills the Stripe set leans on, one at a time: GROUP BY exercises · JOIN practice · Window function practice · CTE practice · Date function practice — or browse every SQL practice question.
Every question in the Stripe set, one page each with the schema and a hint: JOIN with a Filter · LEFT JOIN: Watch the NULLs Appear · Counting Across a JOIN · Transactions at High-Risk Merchants · Category Revenue with Relabeling · Customer Recency Analysis · Membership Tier Revenue Analysis · Monthly Order Trends · Multi-Month Active Customers · Inactive Customers by Tier · Month-over-Month Customer Growth · Most Recent Order Per Customer (ROW_NUMBER) · The Previous Order's Total (LAG) · Running Total of Orders · Chargeback Rate Per Merchant · Busiest Merchants, and What a Tie Does to the Rank · Each Merchant's Share of Its Category · Running Total of Daily Card Spend · Cumulative Distinct Customers Over Time · Running Total Revenue · Year-over-Year Growth · 7-Day Rolling Revenue Average · Multi-CTE Revenue Pipeline · Order Sessionization by Customer · Cumulative Revenue Share (Pareto) · Detect Repeat Buyers Within 7 Days · Customer Lifetime Value · Revenue Share by Category (Window %) · Order Status Dashboard · Anti-Join Pipeline: Unmatched Records · Customer Lifetime Value Pipeline · Order Funnel Conversion · Month-over-Month Revenue Growth · Customer Retention Cohort · Disputed Spend by Risk Tier and Category.
No signup required. No credit card. Open the app and start practicing Stripe SQL patterns right now.
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Preparing for the analytics round? Read SQL for fraud analytics — velocity checks, rolling windows and self-joins, with a self-check quiz that drops you into real banking data.
Interviewing at more than one company? The same patterns carry: Wise · Revolut · Plaid · Ramp — or the full company-by-company interview guide.