Spotify Junior Data Engineer Interview in London
At Spotify, the Junior Data Engineer interview is characterized by Squad-based engineering, product analytics depth, streaming-data specialization. To clear this bar you need foundational SQL fluency and a willingness to learn production systems, built on 0-2 years of production DE work. Below we dig into how this runs out of the London office (London, UK), with cost-of-living-adjusted compensation.
Compensation
$75K–$94K base • $98K–$127K total
Loop duration
3 hours onsite
Rounds
4 rounds
Location
London, UK
Practice problems
Spotify junior data engineer practice set
Problems the Spotify junior data engineer loop tends to ask, surfaced from signals in current job descriptions. Click any to start practicing.
The Legacy Hunt
The data engineering team is sunsetting pipelines that predate a recent infrastructure migration. Surface every unique pipeline name that had at least one run before July 1, {{YEAR}}.
The Solo Act
Given a list of integers, return a list of the values that appear exactly once, preserving input order.
Full Customer Order List
Return first_name, last_name, and country for every customer in customers. Sort alphabetically by first_name, then last_name.
Low-Byte CDN Responses
The CDN team suspects some responses are suspiciously small, possibly indicating truncated or error payloads. Pull all log entries where bytes served is under 5000, showing every available field, ordered from smallest response up.
Rolling 7-day active users
Count distinct users active in the trailing 7 days for each date. Product analytics staple.
Walk into Spotify knowing the system design pattern they'll test.
London, UK
Spotify in London
Largest European DE market. Comp is materially lower than US metros but higher than anywhere else in Europe. Visa sponsorship is routine for senior roles.
Spotify pays about 35% less in London than its reference band; this maps to local market compensation norms. Spotify sponsors visas for junior data engineer hires in London as a matter of course. The interview loop itself is identical to Spotify's global process in London; local variation shows up in team and compensation.
A Clean Number for Every Merchant
Raw payment logs in. Clean merchant summaries out.
Pulled from debriefs where system design separated levels.
The loop
How the interview actually runs
01Recruiter screen
45 minLonger than many peer companies. Spotify wants to understand motivation and squad fit, their squad model means team-specific chemistry is real.
- →Research the squad: Discovery, Podcasts, Personalization, Marketplace
- →Streaming + music + cultural context is genuinely a signal, don't pretend not to care
- →Ask about squad autonomy. Spotify's squad model is core culture
02Technical phone screen
60 minSQL + a product analytics scenario. 'A key metric dropped 10% yesterday. Figure out why.' Spotify tests analytical thinking alongside SQL fluency.
- →Practice drill-down analysis: segment by platform, country, cohort, time of day
- →Be explicit about your investigation order. Spotify interviewers watch it
- →Know music-specific metrics: MAU/DAU, stream-through rate, skip rate
03Onsite: data system design
60 minDesign a streaming-data pipeline. Music play events, podcast engagement, recommendation feedback loops. Spotify's scale is music-data-specific (billions of streams/day).
- →Event-stream architecture is central: Kafka + Flink or similar
- →Discuss exactly-once semantics for billing/royalty systems
- →Recommendation feedback loops: feature stores, real-time scoring
04Onsite: squad fit + behavioral
60 minBlend of technical deep-dive and cultural fit. Spotify's squad model means team chemistry is tested as much as individual capability.
- →Collaboration stories within squads, autonomy matters
- →Spotify's 'we belong' mantra, inclusive culture stories land
- →Stories about prioritizing squad autonomy over centralized standards
Level bar
What Spotify expects at Junior Data Engineer
SQL foundations
Junior rounds weight SQL the heaviest. Expect multi-table joins, aggregations, window functions, and one harder query involving self-joins or recursive CTEs. You do not need to design systems at this level, but you do need SQL to be reflexive.
Learning orientation
Interviewers probe how you pick up new tools. A strong story about learning a new stack in a prior role (even an internship or side project) can outweigh gaps in production experience.
Basic pipeline awareness
You should know what ETL vs ELT means, what a data warehouse is, and why idempotency matters, even if you have not built a production pipeline yourself.
Spotify-specific emphasis
Spotify's loop is characterized by: Squad-based engineering, product analytics depth, streaming-data specialization. Calibrate your preparation to that, generic FAANG prep will not close the gap on company-specific expectations.
Behavioral
How Spotify frames behavioral rounds
Playful
Spotify's culture values creativity and experimentation. Engineers who take themselves too seriously stand out negatively.
Collaborative
Squad model depends on cross-role collaboration: engineers, data scientists, product managers working tightly.
Innovative
Spotify's product is built on novel experiences (Discover Weekly, Wrapped). Engineers are expected to bring new ideas, not just execute.
Passionate
Cultural alignment with music, podcasts, and audio matters. Engineers who clearly use the product deeply are valued.
Prep timeline
Week-by-week preparation plan
Foundations and gap analysis
- ·Do 10 medium SQL problems. Note which patterns feel slow
- ·Write out 2-3 behavioral stories per value, Spotify weights this round heavily
- ·Read Spotify's public engineering blog for recent architecture patterns
- ·Shore up data engineering foundations: SQL, Python, one warehouse (Snowflake/BigQuery/Redshift)
SQL and coding fluency
- ·Practice window functions until DENSE_RANK, ROW_NUMBER, LAG, LEAD are reflex
- ·Do 20+ Spotify-style problems in their domain
- ·Time yourself: 25 min per medium, 35 min per hard
- ·Record yourself narrating approach aloud, communication is graded
Pipeline awareness and behavioral depth
- ·Review pipeline architecture basics: idempotency, partitioning, backfill
- ·Practice explaining a pipeline you've worked on end-to-end in 5 minutes
- ·Refine behavioral stories based on mock feedback
- ·Do 10 more SQL problems at medium difficulty
Behavioral polish and mock loops
- ·Rehearse every story out loud. Cut to 2-3 minutes each
- ·Run 2 full mock loops with a mid-level DE or coach
- ·Identify your 3 weakest behavioral areas and draft additional stories
- ·Review recent Spotify news or earnings call for fresh talking points
Taper and logistics
- ·No new content. Review your notes only
- ·Sleep. Mental energy matters more than one more practice problem
- ·Confirm logistics: laptop charged, shared-doc tool tested, snack and water nearby
- ·Remember: interviewers want to find reasons to hire you, not to reject you
See also
Related pages on Spotify's loop
FAQ
Common questions
- How much does a Spotify Junior Data Engineer in London make?
- Total compensation for Spotify Junior Data Engineer in London ranges $75K–$94K base • $98K–$127K total. Ranges shift by team and negotiation.
- Does Spotify actually hire data engineers in London?
- Yes, Spotify maintains a London office and hires Junior Data Engineer data engineers there. Team assignment may be office-locked or global; confirm with the recruiter before the loop.
- How is the Junior Data Engineer loop different from other levels at Spotify?
- Round structure is shared across levels; what changes is what each round tests. For Junior Data Engineer the emphasis is foundational SQL fluency and a willingness to learn production systems, with particular attention to SQL fundamentals, learning orientation, and basic pipeline awareness.
- How long should I prepare for the Spotify Junior Data Engineer interview?
- 6-8 weeks of focused prep is typical for candidates already working as a DE. Less than 4 weeks is tight; the behavioral story bank usually takes longer than candidates expect.
- Does Spotify interview data engineers differently than software engineers?
- Yes. DE loops at Spotify weight SQL heavier, include pipeline/system-design rounds tuned to data workloads, and probe for production data experience (ingestion patterns, data quality, backfill) that generalist SWE loops skip.