Master the SQL patterns Uber asks across Data Analyst, Data Scientist, and Analytics Engineer roles. Practice marketplace metrics, surge pricing, and two-sided retention with real datasets and AI tutoring.
18 challenges
Uber patterns
Real datasets
Rides & marketplaces
AI tutor
Step-by-step hints
What this page is: we have no dated public source for how Uber 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 Uber works with. What is ours: the questions are SQL Quest challenges and the topic emphasis is our editorial judgement, not a measured breakdown of Uber's interview. Pages with dated sources say so in this spot.
The 18 challenges tagged Uber in the SQL Quest bank, with every raw challenge tag resolved to the 9 canonical skills. Each share is the portion of those 18 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 Uber’s interview.
These are SQL Quest challenges chosen because their SQL matches the work — Querying Basics, Aggregation & Grouping, Window Functions. They are not questions Uber 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 Uber and not a description of its process. We have no dated, citable source for how Uber 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 Uber set leans on, one at a time: GROUP BY exercises · Window function practice · Date function practice · CTE practice · JOIN practice — or browse every SQL practice question.
Every question in the Uber set, one page each with the schema and a hint: Department Roster with GROUP_CONCAT · Category Revenue with Relabeling · Customer Recency Analysis · Monthly Order Trends · Window Functions: ROW_NUMBER · Monthly Order Volume in 2024 · Month-over-Month Customer Growth · Running Total Revenue · Order Sessionization by Customer · First and Last Order per Customer · Detect Repeat Buyers Within 7 Days · Consecutive IDs · Daily Active Customers · Engagement Streaks (3+ Orders, ≤7-Day Gaps) · Deduplicate Orders with ROW_NUMBER · Self-Join: Repeat Orders Within a Week · Month-over-Month Revenue Growth · Island Length Classification.
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Interviewing at more than one company? The same patterns carry: Airbnb · Shopify · Spotify — or the full company-by-company interview guide.