What level is Data Engineer at Amazon?

Data Engineer maps to L5 on Amazon's engineering ladder. This is an individual contributor level; expectations focus on shipped production pipelines end-to-end and can debug them when they break.

How much does a Amazon Data Engineer in London make?

Total compensation for Amazon Data Engineer in London ranges $101K–$120K base • $150K–$189K total (L5). Ranges shift by team and negotiation.

Does Amazon actually hire data engineers in London?

Yes, Amazon maintains a London office and hires 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 Amazon Data Engineer interview?

Plan for 6-8 weeks of prep if you're already a working DE. Under 4 weeks rushes the behavioral prep, which takes the most time.

Does Amazon interview data engineers differently than software engineers?

They differ meaningfully. Amazon's DE loop has heavier SQL, replaces the general system-design with a data-specific one (pipelines, warehouse design), and expects production data ops experience.

Interview Guide

Amazon Data Engineer Interview in London (L5)

Q: How is the Data Engineer loop different from other levels at Amazon?

The rounds look similar, but the bar calibrates to seniority. Data Engineer is evaluated on shipped production pipelines end-to-end and can debug them when they break. Questions at this level probe production pipeline ownership and on-call debugging.

Hiring for Data Engineer at Amazon (L5) runs Leadership Principles woven into every round, with a Bar Raiser holding veto power. The hiring bar is shipped production pipelines end-to-end and can debug them when they break; the median candidate brings 2-5 years of DE experience. This guide covers the London (London, UK) hiring office, including local compensation bands and market context.

Compensation

$101K–$120K base • $150K–$189K total (L5)

Loop duration

3.8 hours onsite

Rounds

5 rounds

Location

London, UK

Practice problems

Amazon data engineer practice set

4 problems

Amazon data engineer practice set, mapped from predicted domain emphasis. Tap into any problem to work it in the live environment.

SQLeasy~10 min

Metric Range Per Group

HR suspects some departments have much wider performance variance than others. For each department, compute the spread between its highest and lowest metric values, ordered alphabetically by department.

Open in practice environment→

Pythonhard~5 min

The Overlap

Your monitoring system logs server maintenance as `[start, end]` minute ranges, and windows that overlap or sit back-to-back really describe one continuous outage. Collapse the `windows` so any that overlap or touch at an endpoint become a single range, and return them ordered by start time. Two windows touch when one ends exactly where the next begins.

Open in practice environment→

Modelingmedium~20 min

A/B Experiment Assignment Schema

We run product experiments across our consumer app. When a user is assigned to an experiment, we need to track which variant they saw and when. Analysts need to compute metric lifts between variants. Design the data model to support experimentation analysis.

Open in practice environment→

SQLmedium~10 min

Subscribers Without Premium

Pull basic-plan subscribers who never upgraded to premium from the subscriptions data. The retention team wants to run a winback campaign targeting this group.

Open in practice environment→

Top 2 sellers by revenue in each marketplace

Classic DE round opener. Window function + partition. Edit to tweak the threshold.

1	WITH seller_totals AS (
2	SELECT
3	marketplace,
4	seller_id,
5	SUM(amount) AS revenue
6	FROM seller_orders
7	GROUP BY marketplace, seller_id
8	),
9	ranked AS (
10	SELECT
11	marketplace,
12	seller_id,
13	revenue,
14	DENSE_RANK() OVER (
15	PARTITION BY marketplace
16	ORDER BY revenue DESC
17	) AS rk
18	FROM seller_totals
19	)
20
21	SELECT
22	marketplace,
23	seller_id,
24	revenue
25	FROM ranked
26	WHERE rk <= 2
27	ORDER BY marketplace, revenue DESC

Prepare for the interview

01 / Open invite

02min.

Walk into Amazon knowing the system design pattern they'll test.

a Amazon system design query, the same shape a screen would give you.

The diff against expected. Where ties broke. What you missed.

sandbox

1source → bronze → silver → gold

2 ingest : CDC + Kafka

3 transform : dbt + Airflow

4 serve : Snowflake

Execute your solution0.4s avg.

AmazonInterview question

Solve a Amazon problem

London, UK

Amazon 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.

Offers in London typically trail the reference band by around 35%, reflecting a lower cost of living. For international candidates, Amazon routinely sponsors work permits for data engineer hires in London. London candidates run the same loop as global peers; the differences show up in team assignment and local comp calibration.

Prepare for the interview

03 / From the bank03 of many

03hand-picked.

Five Years of Cron Jobs

Hard10 min

Half the jobs run on cron. Half run on events. All of it has to move.

Open the sandbox

Pick your level

AmazonL3The Bucket Full of Resumes NetflixL4The Clicks We Throw Away AirbnbL5The Clock That Runs Two Ways

Pulled from debriefs where system design separated levels.

The loop

How the interview actually runs

01Recruiter screen

30 min

Logistics, team fit, and a light Leadership Principle question. Recruiters confirm seniority expectations before booking the loop. Misalignment here can downlevel the loop.

→Have a 60-second pitch that names 2-3 concrete data systems you've built
→Confirm the team. Amazon has hundreds of DE teams across AWS, Retail, Ads, Alexa, Prime Video, Pharmacy
→Ask about the comp band early to avoid end-of-loop misalignment

02Technical phone screen

60 min

One SQL problem, one Python or pipeline design problem, and 10-15 min of Leadership Principle questions. The SQL is harder than the Online Assessment, expect multi-step window functions or self-joins.

→Narrate approach before writing code. Amazon grades process, not just the final answer
→Name the LP before telling the story
→Prepare at least 2 stories per LP; follow-ups probe a third story on the same theme

03Onsite: SQL deep-dive

60 min

Two to three SQL problems with escalating difficulty, usually in Amazon contexts (seller performance, order fulfillment, inventory). Ends with 10 min of LP questions.

→Practice window functions across large partition cardinalities
→Be ready to rewrite correlated subqueries as joins and vice versa
→When asked about optimization, mention partition pruning and columnar storage

04Onsite: Bar Raiser

60 min

An interviewer from outside the hiring team with veto power. Heaviest on Leadership Principles, with one harder technical problem. Tests whether you raise Amazon's hiring bar.

→Bring a story where you were wrong and had to change course
→Quantify impact: cost saved, latency reduced, users affected
→If you don't know something, say so. Fabricating kills the loop faster than any technical gap

Level bar

What Amazon expects at Data Engineer

Pipeline ownership

Mid-level DEs own pipelines end-to-end. Interviewers expect stories about designing, deploying, and maintaining a data pipeline that has been in production for 6+ months.

SQL + Python or Spark fluency

SQL is the floor. Most teams also expect fluency in either Python for data manipulation (pandas, airflow DAGs) or Spark for larger-scale processing.

On-call debugging

You should have concrete stories about production incidents: what alert fired, how you diagnosed, what you fixed, and what post-mortem action you owned.

Amazon-specific emphasis

Amazon's loop is characterized by: Leadership Principles woven into every round, with a Bar Raiser holding veto power. Calibrate your preparation to that, generic FAANG prep will not close the gap on company-specific expectations.

Behavioral

How Amazon frames behavioral rounds

Dive Deep

The most relevant LP for data engineers. Amazon wants DEs who trace anomalies through 3+ layers of the stack instead of patching symptoms.

“Tell me about a time you found a data quality issue that others had missed.”

Ownership

You built it, you own it, including on-call and long-term maintenance. Ownership extends beyond your explicit scope when dependencies break.

“Describe a situation where you went beyond your role to solve a problem.”

Bias for Action

Speed beats perfection. Amazon wants DEs who ship V1 in 2 weeks rather than a perfect solution in 3 months.

“Tell me about a time you made a decision without having all the information.”

Earn Trust

Trust comes from delivery and transparency. Bar Raisers test whether you can admit mistakes and communicate setbacks without spinning.

“Tell me about a time you delivered bad news to a stakeholder.”

Prep timeline

Week-by-week preparation plan

8-10 weeks out

Foundations and gap analysis

·Do 10 medium SQL problems. Note which patterns feel slow
·Write out 2-3 behavioral stories per value, Amazon weights this round heavily
·Read Amazon's public engineering blog for recent architecture patterns
·Review your prior production work, pick 3-5 projects you can discuss in depth

6 weeks out

SQL and coding fluency

·Practice window functions until DENSE_RANK, ROW_NUMBER, LAG, LEAD are reflex
·Do 20+ Amazon-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 Amazon 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 interview guides

Amazon Data Engineer (global guide)→Amazon Data Engineer in Seattle→Amazon Data Engineer Salary→Amazon Interview Guide→Data Engineer Interview Prep (hub)→

FAQ

Common questions

What level is Data Engineer at Amazon?: Data Engineer maps to L5 on Amazon's engineering ladder. This is an individual contributor level; expectations focus on shipped production pipelines end-to-end and can debug them when they break.
How much does a Amazon Data Engineer in London make?: Total compensation for Amazon Data Engineer in London ranges $101K–$120K base • $150K–$189K total (L5). Ranges shift by team and negotiation.
Does Amazon actually hire data engineers in London?: Yes, Amazon maintains a London office and hires Data Engineer data engineers there. Team assignment may be office-locked or global; confirm with the recruiter before the loop.
How is the Data Engineer loop different from other levels at Amazon?: The rounds look similar, but the bar calibrates to seniority. Data Engineer is evaluated on shipped production pipelines end-to-end and can debug them when they break. Questions at this level probe production pipeline ownership and on-call debugging.
How long should I prepare for the Amazon Data Engineer interview?: Plan for 6-8 weeks of prep if you're already a working DE. Under 4 weeks rushes the behavioral prep, which takes the most time.
Does Amazon interview data engineers differently than software engineers?: They differ meaningfully. Amazon's DE loop has heavier SQL, replaces the general system-design with a data-specific one (pipelines, warehouse design), and expects production data ops experience.