Amazon Data Engineer Interview (L5)
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.
Compensation
$155K–$185K base • $230K–$290K total (L5)
Loop duration
3.8 hours onsite
Rounds
5 rounds
Location
Seattle, Arlington, NYC, Bay Area, remote for select teams
Compensation
Amazon Data Engineer total comp
Offer-report aggregate, 2019-2026. Level mapped: L4. Typical experience: 3-9 years (median 5).
25th percentile
$156K
Median total comp
$180K
75th percentile
$216K
Median base salary
$136K
Median annual equity
$30K
Median total comp by year
Tech stack
What Amazon data engineers actually use
What Amazon currently advertises as required for data engineer data engineer roles. Chips link into tool-specific interview guides.
IcebergWalk into Amazon knowing the Python pattern they'll test.
Round focus
Domain concentration by round
Per-round concentration of each domain in Amazon's interview, derived from the skills emphasized across 85 current data engineer postings. Higher bars mean more questions of that type in that round.
Online Assessment
Phone Screen
Onsite Loop
Practice problems
Amazon data engineer practice set
Amazon data engineer practice set, mapped from predicted domain emphasis. Tap into any problem to work it in the live environment.
Active Duo
The growth team is building a cross-engagement segment of users who both make purchases and log browsing sessions on the platform. Return a deduplicated list of usernames for users with activity in both areas.
Quantile Calculator
Given a list of numbers and percentile (0-100), return the value at that percentile using linear interpolation. The index is percentile / 100 * (n - 1); if fractional, linearly interpolate between the floor and ceiling indices of the sorted values.
The Balance Always Reconciles
We're a consumer lending company offering personal loans, auto loans, and mortgages, and each of those products carries its own standard interest rate and term length. A customer can hold several loans at once, and every payment lands as its own transaction against a loan. Design a schema that lets the operations team read each loan's outstanding balance from those payments and the risk team flag delinquent accounts.
Users Who Churned in February
Find all users who had sessions in January 2026 but none in February 2026.
Top 2 sellers by revenue in each marketplace
Classic DE round opener. Window function + partition. Edit to tweak the threshold.
Pulled from debriefs where Python parsing was the gate.
The loop
How the interview actually runs
01Recruiter screen
30 minLogistics, 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 min1 SQL problem, 1 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 min2 to 3 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 minAn interviewer from outside the hiring team with veto power. Heaviest on Leadership Principles, with one harder technical problem. Checks 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 L5 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.
Ownership
You built it, you own it, including on-call and long-term maintenance. Ownership extends beyond your explicit scope when dependencies break.
Bias for Action
Speed beats perfection. Amazon wants DEs who ship V1 in 2 weeks rather than a perfect solution in 3 months.
Earn Trust
Trust comes from delivery and transparency. Bar Raisers check whether you can admit mistakes and communicate setbacks without spinning.
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, 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
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 your approach aloud. Interviewers weigh how you explain it, not only what you write
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 Amazon 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 interview guides
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 make?
- Based on 1,315 offer samples covering 2019-2026, Amazon Data Engineer sees $156K at the 25th percentile, $180K at the median, and $216K at the 75th percentile, median base $136K and median annual equity $30K. Typical experience range: 3-9 years..
- 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.