OpenAI's data roles are product-analytics heavy: usage cohorts, retention curves, sessionization, and experiment readouts. Practice those exact SQL shapes on runnable datasets with AI tutoring.
21 challenges
OpenAI-pattern set
Real datasets
Usage-event tables
AI tutor
Step-by-step hints
What this page is: we have no dated public source for how OpenAI 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 OpenAI works with. What is ours: the questions are SQL Quest challenges and the topic emphasis is our editorial judgement, not a measured breakdown of OpenAI's interview. Pages with dated sources say so in this spot.
The 21 challenges tagged OpenAI in the SQL Quest bank, with every raw challenge tag resolved to the 9 canonical skills. Each share is the portion of those 21 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 OpenAI’s interview.
These are SQL Quest challenges chosen because their SQL matches the work — Querying Basics, Window Functions, Aggregation & Grouping. They are not questions OpenAI 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 OpenAI and not a description of its process. We have no dated, citable source for how OpenAI 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 OpenAI set leans on, one at a time: Window function practice · GROUP BY exercises · CTE practice · Date function practice · JOIN practice — or browse every SQL practice question.
Every question in the OpenAI set, one page each with the schema and a hint: Your First Window Function · Window Functions: ROW_NUMBER · Multi-Month Active Customers · Month-over-Month Customer Growth · Most Recent Order Per Customer (ROW_NUMBER) · Second-Highest Earner Per Department (ROW_NUMBER) · The Previous Order's Total (LAG) · Running Total of Orders · Cumulative Distinct Customers Over Time · Year-over-Year Growth · Order Sessionization by Customer · First and Last Order per Customer · Cumulative Revenue Share (Pareto) · Detect Repeat Buyers Within 7 Days · Fare Percentile Ranking · Engagement Streaks (3+ Orders, ≤7-Day Gaps) · Top-N Products per Category · Deduplicate Orders with ROW_NUMBER · Month-over-Month Revenue Growth · Department Salary Percentile Buckets · Customer Retention Cohort.
No signup required. No credit card. Open the app and start practicing OpenAI-pattern SQL right now.
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Interviewing at more than one company? The same patterns carry: Anthropic · NVIDIA · Databricks — or the full company-by-company interview guide.