Master the SQL patterns Airbnb asks across Data Analyst, Data Scientist, and Analytics Engineer roles. Practice booking funnels, host retention, and market analytics with real datasets and AI tutoring.
27 challenges
Airbnb patterns
Real datasets
Bookings & listings
AI tutor
Step-by-step hints
What this page is: we have no dated public source for how Airbnb 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 Airbnb works with. What is ours: the questions are SQL Quest challenges and the topic emphasis is our editorial judgement, not a measured breakdown of Airbnb's interview. Pages with dated sources say so in this spot.
The 27 challenges tagged Airbnb in the SQL Quest bank, with every raw challenge tag resolved to the 9 canonical skills. Each share is the portion of those 27 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 Airbnb’s interview.
These are SQL Quest challenges chosen because their SQL matches the work — Querying Basics, Aggregation & Grouping, Window Functions. They are not questions Airbnb 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 Airbnb and not a description of its process. We have no dated, citable source for how Airbnb 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 Airbnb set leans on, one at a time: GROUP BY exercises · Window function practice · CTE practice · Date function practice · JOIN practice — or browse every SQL practice question.
Every question in the Airbnb set, one page each with the schema and a hint: Below Department Average · LEFT JOIN NULL Semantics: Inactive Customers · Category Revenue with Relabeling · Quarterly Hiring Cohort Report · Customer Recency Analysis · Monthly Order Trends · Monthly Order Volume in 2024 · Multi-Month Active Customers · Month-over-Month Customer Growth · Cumulative Distinct Customers Over Time · Running Total Revenue · Year-over-Year Growth · 7-Day Rolling Revenue Average · Order Sessionization by Customer · First and Last Order per Customer · Detect Repeat Buyers Within 7 Days · Salary Lead-Lag Gap Within Department · Daily Active Customers · Engagement Streaks (3+ Orders, ≤7-Day Gaps) · Deduplicate Orders with ROW_NUMBER · Customer Lifetime Value Pipeline · Order Funnel Conversion · Self-Join: Repeat Orders Within a Week · Month-over-Month Revenue Growth · Island Length Classification · Customer Retention Cohort · Year-over-Year Movie Rating Trends.
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Interviewing at more than one company? The same patterns carry: Uber · Shopify · Spotify — or the full company-by-company interview guide.