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 in Seattle make?

Total compensation for Amazon Junior Data Engineer in Seattle ranges $115K–$147K base • $156K–$202K total (L4). Ranges shift by team and negotiation.

Does Amazon actually hire data engineers in Seattle?

Yes, Amazon maintains a Seattle 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 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

Amazon Junior Data Engineer Interview in Seattle (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. Details on the Seattle office (Seattle / Bellevue, WA) follow, including compensation calibrated to the local market.

Compensation

$115K–$147K base • $156K–$202K total (L4)

Loop duration

3.8 hours onsite

Rounds

5 rounds

Location

Seattle / Bellevue, WA

Tech stack

What Amazon junior data engineers actually use

Across 12 open roles

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

EMR12Redshift12S311Kinesis11AWS11Lambda11Glue10Spark6 Hive5 Hadoop4 Airflow3Athena2DynamoDB1CI/CD1

Round focus

Domain concentration by round

Across 12 job descriptions

Where each domain tends to come up in Amazon's loop, derived from 12 current junior data engineer job descriptions. Longer bars mean heavier weight.

Online Assessment

Python91%

SQL41%

Architecture9%

Spark7%

Modeling4%

Phone Screen

SQL73%

Python66%

Architecture29%

Spark11%

Modeling8%

Onsite Loop

Architecture55%

Modeling39%

SQL32%

Python32%

Spark9%

Prepare for the interview

01 / Open invite

02min.

Walk into Amazon knowing the Python pattern they'll test.

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

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

sandbox

1def sessionize(events):

2 sessions = []

3 for e in events:

4 if gap_minutes(e) > 30:

Execute your solution0.4s avg.

AmazonInterview question

Solve a Amazon problem

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.

SQLhard~22 min

Users Who Churned in February

Find all users who had sessions in January {{YEAR}} but none in February {{YEAR}}.

Open in practice environment→

Pythonhard~5 min

Data Quality Report

Given a list of record dicts, return a dict per column name with 'null_count' and 'non_null_count'. Consider a value null when it is Python None.

Open in practice environment→

Modelingmedium~20 min

Loan Application Reporting Schema

We're a lending platform. Customers submit loan applications. Our risk team reviews each application and either approves or declines it. Approved applications may or may not result in funded loans, since the customer must accept the offer. The analytics team needs approval rate breakdowns by customer segment. Design the data model.

Open in practice environment→

SQLmedium~5 min

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.

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

Seattle / Bellevue, WA

Amazon in Seattle

No state income tax. AWS and Azure anchor the DE market, with dense mid-to-senior hiring across Amazon, Microsoft, and their ecosystem.

Seattle comp lands about 8% below the reference band in line with local market rates. The Seattle office's interview loop mirrors the global loop structure; team assignment and comp-band negotiation are the main local variables.

Prepare for the interview

03 / From the bank03 of many

03hand-picked.

The Bouncer

Easy15 min

Every door has a guest list.

Open the sandbox

Pick your level

AmazonL3Word Counter MicrosoftL4Subarray Signal AppleL5The Indivisibles

Pulled from debriefs where Python parsing was the gate.

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 Junior Data Engineer (global guide)→Amazon Data Engineer in Seattle→Amazon Senior Data Engineer in Seattle→Amazon Staff Data Engineer in Seattle→Amazon Principal Data Engineer in Seattle→Amazon Data Engineer Salary→Amazon Interview Guide→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 in Seattle make?: Total compensation for Amazon Junior Data Engineer in Seattle ranges $115K–$147K base • $156K–$202K total (L4). Ranges shift by team and negotiation.
Does Amazon actually hire data engineers in Seattle?: Yes, Amazon maintains a Seattle 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 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.