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 1,319 sampled offers from 2019-2026, Amazon Junior Data Engineer total comp comes in at $160K median, ranging from $122K to $201K, median base $123K and median annual equity $25K. Typical experience range: 3-10 years..

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.

Interview Guide · 2026

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

Across 1,319 samples

Offer-report aggregate, 2019-2026. Level mapped: L4. Typical experience: 3-10 years (median 5).

25th percentile

$122K

Median total comp

$160K

75th percentile

$201K

Median base salary

$123K

Median annual equity

$25K

Median total comp by year

2020

$151K n=27

2021

$157K n=195

2022

$160K n=222

2023

$143K n=124

2024

$150K n=108

2025

$164K n=347

2026

$199K n=295

Tech stack

What Amazon junior data engineers actually use

Across 104 open roles

These are the tools that show up in Amazon's junior data engineer DE job descriptions right now. Click any chip to drop into an interview prep page for it.

AWS98EMR97Redshift88 SQL88Glue86S381Lambda81Kinesis80 Python74 Scala53 Spark52Java41Hive40 Hadoop39Informatica22

Round focus

Domain concentration by round

Across 104 job descriptions

Where each domain tends to come up in Amazon's loop, derived from 104 current junior data engineer job descriptions. Longer bars mean heavier weight.

Online Assessment

Python89%

SQL39%

Architecture15%

Phone Screen

SQL66%

Python66%

Architecture31%

Modeling9%

Onsite Loop

Architecture67%

Modeling33%

Python29%

SQL28%

Practice problems

Amazon junior data engineer practice set

4 problems

Interview problems predicted for Amazon junior data engineers based on their actual job descriptions. Click any problem to work it in a live coding environment.

Pythonmedium~20 min

The Coin Vault

Given a target amount and a list of coin denominations, return the minimum coins needed using a greedy strategy: repeatedly take the largest coin that does not exceed the remaining amount. Return -1 if the greedy approach cannot make exact change.

Open in practice environment→

Modelingmedium~25 min

Housing Marketplace Analytics

We run a housing marketplace. Sellers list properties, buyers view listings and submit leads. We need to measure conversion rate from view to lead by location and property type. Design the data model.

Open in practice environment→

Architecturemedium~25 min

The Queue That Wouldn't Stop Growing

Your streaming video event pipeline shows consumer lag spiking from near-zero to over 500,000 messages within two hours. You need to diagnose whether the cause is a producer burst or a consumer slowdown, then design a monitoring and auto-remediation system that can detect, alert on, and automatically recover from future lag events.

Open in practice environment→

Modelingmedium~25 min

Employee Application Time Tracking

We need to track how much time employees spend in each application. HR wants daily summaries of time-per-employee-per-application, and wants to flag any employee spending more than 10 hours/day in a single application. Design the schema to capture this data.

Open in practice environment→

Try itTop 2 sellers by revenue in each marketplace

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

top_sellers.sql

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WITH seller_totals AS (
  SELECT marketplace, seller_id, SUM(amount) AS revenue
  FROM seller_orders
  GROUP BY marketplace, seller_id
),
ranked AS (
  SELECT marketplace, seller_id, revenue,
    DENSE_RANK() OVER (
      PARTITION BY marketplace ORDER BY revenue DESC
    ) AS rk
  FROM seller_totals
)
SELECT marketplace, seller_id, revenue
FROM ranked
WHERE rk <= 2
ORDER BY marketplace, revenue DESC;
WITH seller_totals AS (
  SELECT marketplace, seller_id, SUM(amount) AS revenue
  FROM seller_orders
  GROUP BY marketplace, seller_id
),
ranked AS (
  SELECT marketplace, seller_id, revenue,
    DENSE_RANK() OVER (
      PARTITION BY marketplace ORDER BY revenue DESC
    ) AS rk
  FROM seller_totals
)
SELECT marketplace, seller_id, revenue
FROM ranked
WHERE rk <= 2
ORDER BY marketplace, revenue DESC;

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

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 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.

“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 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
·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+ 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

Adjacent guides to check

Amazon Data Engineer→Amazon Senior Data Engineer→Amazon Staff Data Engineer→Amazon Principal Data Engineer→Meta Junior Data Engineer→Google Junior Data Engineer→Microsoft Junior Data Engineer→Amazon Data Engineer Salary→Data Engineer Interview Prep (hub)→

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 1,319 sampled offers from 2019-2026, Amazon Junior Data Engineer total comp comes in at $160K median, ranging from $122K to $201K, median base $123K and median annual equity $25K. Typical experience range: 3-10 years..
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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Published 2026-04-20Updated 2026-04-20