Company Interview Guide
Spotify processes billions of streaming events daily to power personalized recommendations, Wrapped campaigns, and royalty payments. Their DE interviews focus on event-driven architecture, GCP/BigQuery expertise, and the autonomous engineering culture that defines Spotify squads. Here is what to expect and how to prepare.
Three stages from recruiter call to offer.
Initial conversation about your experience and motivation for joining Spotify. The recruiter evaluates your background with event-driven data systems and your interest in music, podcasts, or media technology. Spotify's data platform team handles billions of events daily from streaming, search, and ad interactions. They look for candidates who care about both technical excellence and product impact.
SQL and Python problems set in a music streaming context. Expect questions about user engagement metrics, playlist analytics, and event processing. Spotify values clean, readable code and clear communication of your approach. The interviewer also evaluates how you think about data quality in event streams.
Four rounds covering system design, SQL deep dive, coding, and a values interview. System design questions at Spotify involve recommendation pipelines, event processing at scale, and data platform architecture. The values interview evaluates collaboration, innovation, and alignment with Spotify's band manifesto. Each interviewer provides independent feedback.
Real question types from each round. The guidance shows what the interviewer looks for.
Filter stream_events where play_duration >= 30. Count DISTINCT user_id per song_id. ORDER BY unique_listeners DESC LIMIT 10. Discuss why 30 seconds is the industry threshold for a 'play' and how to handle repeated plays.
Define skip as play_duration < 15 AND user_action = 'skip'. Group by genre, compute skips / total_plays. Discuss whether autoplay skips should count differently than manual skips.
Use conditional aggregation across multiple event types. Normalize each metric (0 to 1), then weighted average. Discuss how to handle new users with sparse data and whether to use percentile-based normalization.
Read from source, deduplicate using a set or merge key, join to track dimension, group by track_id and date, write partitioned output. Discuss idempotency and how to handle late-arriving events in the next day's partition.
Year-long event aggregation from streaming events. Pre-compute per-user summaries (top artists, genres, minutes listened) incrementally. Discuss the burst of reads on launch day, caching strategy, and how to handle users who listen on multiple devices.
Kafka for event ingestion, feature store for user profiles, ML model serving for recommendations. Discuss cold-start problem for new users, feedback loops (user skips recommended songs), and latency requirements for real-time updates.
Fact: stream_events (user_id, track_id, duration, timestamp, context). Dimensions: tracks, artists, albums, playlists. Discuss the dual purpose: anonymized aggregates for ML features vs precise per-play records for financial reporting. Rights ownership can be complex (multiple writers, labels).
Show proactive engineering: identified the scaling bottleneck before it caused outages. Describe the investigation, the solution, and the measured improvement. Spotify values engineers who improve systems without being asked.
What makes Spotify different from other companies.
Everything at Spotify generates events: plays, skips, searches, playlist edits, ad impressions. Know event-driven patterns: event sourcing, pub/sub messaging, and how to build reliable pipelines on top of event streams. This is the most common system design context.
Spotify migrated from on-premises Hadoop to Google Cloud. BigQuery is their primary analytics warehouse. Know BigQuery-specific features: nested and repeated fields (STRUCT, ARRAY), UNNEST, partitioned tables, and materialized views. This context helps in both SQL and system design rounds.
Backstage is Spotify's developer portal for managing microservices, data pipelines, and documentation. Understanding Backstage shows you have researched Spotify's engineering culture and care about developer experience, which is a core value.
Spotify organizes into autonomous squads. Data engineers are embedded in squads rather than centralized. Prepare examples of working independently within a team, making local decisions, and collaborating across team boundaries.
Spotify DE interviews test event-driven thinking and GCP expertise. Practice problems that mirror streaming data scenarios.
Practice Spotify-Level SQL