Master the SQL patterns Spotify asks across Data Analyst, Data Scientist, and Analytics Engineer roles. Practice listening sessions, playlist analytics, and premium conversion with real datasets and AI tutoring.
20 challenges
Spotify patterns
Real datasets
Listens & playlists
AI tutor
Step-by-step hints
What this page is: we have no dated public source for how Spotify 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 Spotify works with. What is ours: the questions are SQL Quest challenges and the topic emphasis is our editorial judgement, not a measured breakdown of Spotify's interview. Pages with dated sources say so in this spot.
The 20 challenges tagged Spotify in the SQL Quest bank, with every raw challenge tag resolved to the 9 canonical skills. Each share is the portion of those 20 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 Spotify’s interview.
These are SQL Quest challenges chosen because their SQL matches the work — Querying Basics, Aggregation & Grouping, Subqueries & CTEs. They are not questions Spotify 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 Spotify and not a description of its process. We have no dated, citable source for how Spotify 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 Spotify set leans on, one at a time: GROUP BY exercises · CTE practice · Window function practice · CASE WHEN practice · JOIN practice — or browse every SQL practice question.
Every question in the Spotify set, one page each with the schema and a hint: Recent Hit Movies (2010s) · Genre Financial Report · Genre Box Office Report · Genres Without Blockbusters · Highest Rated by Genre · GROUP BY + HAVING · Conditional Counting with CASE · Cumulative Distinct Customers Over Time · Year-over-Year Growth · 7-Day Rolling Revenue Average · Order Sessionization by Customer · Cumulative Revenue Share (Pareto) · Moving Average with Dynamic Window · Rank Movies by Rating · Genres in Common (INTERSECT) · 3-Movie Rolling Average Revenue · RANK vs DENSE_RANK: Rating Gaps · Sliding Window Max Revenue · Year-over-Year Movie Rating Trends · Earliest Movie per Genre.
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Interviewing at more than one company? The same patterns carry: Uber · Airbnb · Shopify — or the full company-by-company interview guide.