Question 1

What SQL topics does Spotify focus on?

Accepted Answer

Spotify SQL interviews emphasize listening session analytics (plays, skips, completion rate), playlist and library engagement, premium vs free tier conversion, and artist/track performance metrics. Expect session-based datasets where sessionization and window functions play a big role.

Question 2

Does Spotify ask about session data specifically?

Accepted Answer

Yes. Session-level analysis is core to Spotify. Expect at least one question requiring you to sessionize raw play events (30-minute inactivity gaps typically), compute session-level metrics, and reason about engagement on a session basis rather than user basis.

Question 3

How technical are Spotify SQL interviews?

Accepted Answer

Solid intermediate-to-advanced. Spotify loves window functions (LAG, LEAD for session gaps, running totals for listen counts), multi-step CTEs, and correlated subqueries. Data Scientist loops add experimentation framing on top — you compute the metric in SQL, then discuss A/B test implications.

Question 4

What SQL dialect does Spotify use?

Accepted Answer

Spotify's stack is BigQuery-heavy with some Presto/Trino. Interviews accept standard ANSI SQL. Familiarity with DATE_TRUNC, TIMESTAMP_DIFF, window frames, and multi-step CTEs covers what's tested. You don't need to know Spotify-specific internals.

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