Amazon Junior Data Engineer Interview (L4)
At Amazon, the (L4) Junior Data Engineer interview is characterized by Leadership Principles woven into every round, with a Bar Raiser holding veto power. 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.
Compensation
$125K–$160K base • $170K–$220K total (L4)
Loop duration
3.8 hours onsite
Rounds
5 rounds
Location
Seattle, Arlington, NYC, Bay Area, remote for select teams
Compensation
Amazon Junior Data Engineer total comp
Offer-report aggregate, 2022-2026. Level mapped: L3.
25th percentile
$132K
Median total comp
$147K
75th percentile
$166K
Median base salary
$124K
Median total comp by year
Tech stack
What Amazon junior data engineers actually use
These are the tools that show up in Amazon's DE job descriptions right now. Click any chip to drop into an interview prep page for it.
Walk into Amazon knowing the Python pattern they'll test.
Round focus
Domain concentration by round
Where each domain tends to come up in Amazon's loop, derived from 97 current junior data engineer job descriptions. Longer bars mean heavier weight.
Online Assessment
Phone Screen
Onsite Loop
Practice problems
Amazon junior data engineer practice set
Practice problems tagged with Amazon, the company each one was written for, at the junior data engineer level where we have them. Click any problem to work it in a live coding environment.
After the Cutoff
Our release team is auditing production deploys from April onward, regardless of year, to see which services stayed active late in the cycle. Break those deploys down by service, showing how many each had and the longest deployment duration, busiest services first.
The Dominant Signal
You're scanning a device log where each entry is a code a sensor reported during a shift. Return every code that shows up the most times, and when several tie for the top, return all of them in ascending order.
A Number for the Seller
We run an online marketplace where sellers list products, and each product records the date its listing went live and whether that listing is still active, since a seller may delist or relist over time. Every sale is logged line by line with the units sold and the revenue earned. Build the analytics schema behind a daily seller dashboard that shows, for each seller, how many of their listings are currently active and, for each date, the revenue and units their sales earned.
The Ledger and the Live Wire
We run an online retail marketplace that emits about 2 billion order and refund events a day across 300 million listings. Seller-facing inventory and sales dashboards have to reflect a purchase within seconds, while finance needs an exactly-once daily profit-per-product number that stays correct even when refunds land days after the original order. Data science also wants the full raw event history retained for model training.
Top 2 sellers by revenue in each marketplace
Classic DE round opener. Window function + partition. Edit to tweak the threshold.
The Long Run
Say it once, then say how many times it kept saying it.
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 L4 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.
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
- ·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+ 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
Adjacent guides to check
FAQ
Common questions
- What level is Junior Data Engineer at Amazon?
- At Amazon, Junior Data Engineer corresponds to the L4 level. The bar emphasizes foundational SQL fluency and a willingness to learn production systems without people-management responsibilities.
- How much does a Amazon Junior Data Engineer make?
- Looking at 65 sampled offers from 2022-2026, Amazon Junior Data Engineer total comp comes in at $147K median, ranging from $132K to $166K, median base $124K..
- How is the Junior Data Engineer loop different from other levels at Amazon?
- The format of the loop matches other levels; difficulty and evaluation shift to foundational SQL fluency and a willingness to learn production systems, and questions at this level dig into SQL fundamentals, learning orientation, and basic pipeline awareness.
- How long should I prepare for the Amazon Junior Data Engineer interview?
- Most working DEs find 6-8 weeks is about right. The technical prep scales with experience; the behavioral story bank is where candidates underestimate time.
- Does Amazon interview data engineers differently than software engineers?
- Yes, the DE track at Amazon emphasizes SQL depth, warehouse and pipeline design, and real production data experience (late data, backfills, quality checks), which generalist SWE loops don't test.
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