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 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.

Interview Guide · 2026

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

Tech stack

What Spotify junior data engineers actually use

Across 4 open roles

Frequency of each tool across Spotify's open DE postings in London. The ones with interview prep pages are live links.

Scala2 dbt2GCP2SQL2 Python1 Snowflake1 Spark1Java1Beam1BigQuery1 Airflow1

Round focus

Domain concentration by round

Across 4 job descriptions

Spotify's round-by-round focus, inferred from 4 active junior data engineer job descriptions. Use this to calibrate which domains to drill for each round.

Online Assessment

Python87%

SQL41%

Architecture17%

Modeling3%

Phone Screen

SQL65%

Python65%

Architecture34%

Modeling10%

Onsite Loop

Architecture67%

Modeling32%

SQL29%

Python29%

Practice problems

Spotify junior data engineer practice set

4 problems

Problems the Spotify junior data engineer loop tends to ask, surfaced from signals in current job descriptions. Click any to start practicing.

SQLeasy~5 min

Top Performing Models

The ML registry tracks model accuracy. Surface all models with accuracy at 0.90 or above. Return all available fields for each qualifying model, sorted from highest accuracy to lowest.

Open in practice environment→

Pythoneasy~15 min

The Cipher Wheel

Given a string s and a dict mapping single characters to single characters, return a new string where each character is replaced by mapping[char] if present, else kept unchanged.

Open in practice environment→

SQLmedium~10 min

Binary Flag Indicators

The feature flag dashboard needs a clean boolean representation for downstream consumers. For each flag, show the flag name, a 1/0 indicator for whether it is enabled, and a 1/0 indicator for whether it is disabled.

Open in practice environment→

Pythonmedium~10 min

Detect Cycle in Sequence

You are given a list of integers where each value at index i is the next index to visit (or -1 to terminate). Starting from index 0, follow the chain and return True if you revisit any index, False otherwise. Out-of-range indices (including -1) count as termination, not a cycle.

Open in practice environment→

Try itRolling 7-day active users

Count distinct users active in the trailing 7 days for each date. Product analytics staple.

rolling_7dau.sql

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WITH dates AS (
  SELECT DISTINCT activity_date FROM activity
)
SELECT
  d.activity_date AS day,
  COUNT(DISTINCT a.user_id) AS rolling_7d_users
FROM dates d
JOIN activity a
  ON a.activity_date <= d.activity_date
  AND julianday(d.activity_date) - julianday(a.activity_date) < 7
GROUP BY d.activity_date
ORDER BY d.activity_date;
WITH dates AS (
  SELECT DISTINCT activity_date FROM activity
)
SELECT
  d.activity_date AS day,
  COUNT(DISTINCT a.user_id) AS rolling_7d_users
FROM dates d
JOIN activity a
  ON a.activity_date <= d.activity_date
  AND julianday(d.activity_date) - julianday(a.activity_date) < 7
GROUP BY d.activity_date
ORDER BY d.activity_date;

Click Run to execute. Edit the code above to experiment.

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.

The loop

How the interview actually runs

01Recruiter screen

45 min

Longer 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 min

SQL + 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 min

Design 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 min

Blend 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.

“Tell me about an unusual approach you tried that didn't come from the obvious path.”

Collaborative

Squad model depends on cross-role collaboration: engineers, data scientists, product managers working tightly.

“Describe a time you worked closely with a non-engineering partner to ship something.”

Innovative

Spotify's product is built on novel experiences (Discover Weekly, Wrapped). Engineers are expected to bring new ideas, not just execute.

“What's a new idea you brought to a team that got shipped?”

Passionate

Cultural alignment with music, podcasts, and audio matters. Engineers who clearly use the product deeply are valued.

“How has your use of Spotify (or similar products) informed your work?”

Prep timeline

Week-by-week preparation plan

8 weeks out

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)

6 weeks out

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

4 weeks out

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

2 weeks out

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

Week of

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

Related pages on Spotify's loop

Spotify Junior Data Engineer (global guide)→Spotify Data Engineer in London→Spotify Senior Data Engineer in London→Spotify Staff Data Engineer in London→Spotify Principal Data Engineer in London→Spotify Junior Data Engineer in New York→Spotify Junior Data Engineer in Stockholm→Spotify Junior Data Engineer in Boston→Spotify Data Engineer Salary→Data Engineer Interview Prep (hub)→

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.

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Published 2026-04-20Updated 2026-04-20