Amazon Data Engineer Interview: The Full Process, Decoded

You searched "Amazon data engineer interview guide" and got seven articles written for software engineers. Helpful. Now you know how to solve binary tree problems that will never appear in your loop, and you still have no idea what actually happens in the SQL round.

This guide is for data engineers specifically: every round, what it tests, how the Bar Raiser fits in, and a prep plan calibrated to the role. Including, yes, a lot of SQL.

What the Loop Looks Like

Amazon runs a consistent hiring loop for data engineering roles:

Recruiter screen (30 min)
Online assessment (2 coding problems + work simulation)
Technical phone screen (45-60 min, SQL + coding)
Virtual onsite (4-6 rounds, 45-60 min each)

The virtual onsite is where most of the work happens. It typically includes a SQL round, a coding/DSA round, a data architecture round, two behavioral rounds, and a Bar Raiser session. Some loops fold the Bar Raiser into another round. Total timeline: three to six weeks.

Round	Focus	Length
Online Assessment	2x coding + simulation	Self-paced
Technical Phone Screen	SQL + coding	45-60 min
SQL Round	Complex queries, optimization, data modeling	45-60 min
Coding Round	DSA, Python	45-60 min
System Design Round	Pipeline architecture, scalability	45-60 min
Behavioral (x2)	Leadership Principles, STAR stories	45-60 min each
Bar Raiser	Any of the above, harder	45-60 min

Online Assessment: Don't Overthink It

Before the phone screen, a timed OA lands in your inbox: two coding problems (easy to medium) plus a work simulation with situational questions tied to Amazon's culture.

The coding problems won't trip you up with a few weeks of prep. The simulation isn't scored pass/fail, but it feeds into recruiter notes. Pick answers that reflect Ownership, Bias for Action, or Dive Deep. Those are the LPs that come up most in these scenarios, and yes, this is already the LP show, even before round one.

The Phone Screen Tests Breadth, Not Depth

Forty-five to sixty minutes with a hiring manager or senior data engineer. Expect a SQL question, a coding question, and a couple of behavioral questions.

The SQL question is usually a medium-complexity join or aggregation. Know the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() cold. Not "I've seen those before" cold. Actually cold. The coding question is typically straightforward Python: arrays, hash maps, maybe a simple graph traversal.

You won't be in a shared IDE. Expect a Google Doc or similar. Write readable code, not terse code. Nobody is impressed by one-liners in a Google Doc.

SQL Is the Round That Catches People Off Guard

This is what separates prepared candidates from overconfident ones. Amazon's SQL bar is high because the actual job involves writing production queries against massive datasets, and they test accordingly.

Common patterns:

Window functions. Month-over-month revenue growth per product. Running totals. Percentile ranking. Expect at least one window function question, probably two.
Complex joins. Multi-table joins with edge cases: customers with zero orders, products with no reviews. LEFT JOIN plus NULL filter shows up repeatedly.
Aggregation with conditions. GROUP BY combined with CASE statements, average ratings per product per month broken by condition.
Optimization. "Here's a slow query, what's wrong?" Be ready to explain indexing choices, scan vs. seek, when to rewrite a subquery as a CTE.

You won't be running queries. You'll write them by hand and talk through your reasoning. Explain your thinking as you write, not after. If the interviewer watches you compose a window function, they're watching how you build the query, not just whether the final answer compiles.

Data modeling comes up too. Design a schema for a simple e-commerce scenario, normalize to 3NF, and discuss when you'd denormalize for query performance. No pressure.

Anime character going increasingly dead-eyed: getting nervous in interview, pronouncing QT as Q.T., SQL as S.Q.L., pi as P.I.

Amazon round two, arrival.

The Coding Bar Is Lower Than You Think

DSA questions for data engineer roles are easier than for SDE roles, but they still appear and still matter.

Common topics: arrays, strings, hash maps, trees, graphs, occasional DP. Difficulty sits at LeetCode medium. One reported pattern: an easy warm-up followed by a medium, sometimes with a SQL question stapled on. Confirm the format with your recruiter since some loops combine coding and SQL into one round.

The language is Python. Walk through test cases, discuss time and space complexity, and adapt when the interviewer changes a constraint. Silence is the failure mode, not the difficulty level. Narrate.

Design a Pipeline, Not a Distributed System

You're designing end-to-end data pipelines, not distributed systems in the abstract. This is the data architecture round, and the framing matters.

Common prompts: a real-time product event ingestion pipeline handling millions of events per hour, an ETL pipeline from S3 to a data warehouse, a monitoring system for a batch job with late-arriving data.

Know the AWS data ecosystem. S3 for storage, Glue or Spark for transformation, Redshift for warehousing, Kinesis or Kafka for streaming, Airflow or Step Functions for orchestration, CloudWatch for monitoring. You don't need to memorize every service, but if you propose "a streaming queue," have an opinion on Kinesis vs. Kafka. Generic answers fail this round. "Something distributed" is not an answer.

Talk through failure modes without being prompted: idempotent loads, retry logic, exactly-once semantics, late data handling. Amazon runs at a scale where these aren't theoretical concerns.

Pepe the frog at a computer: interviewer asks about 80ms vs 600ms latency, same backend, answer is "will send users to Australia"

Not quite the CDN answer they were looking for.

Leadership Principles Show Up Everywhere

This is where technically solid candidates leave points on the table.

Every interviewer is assigned two or three specific LPs to probe, and they score you on them during technical rounds too. Your pipeline walkthrough is also evidence for Dive Deep or Ownership. You are always being assessed against the LPs. Always.

Use STAR: Situation, Task, Action, Result. Numbers help. "I reduced pipeline latency by 40%" is evidence. "I improved the pipeline significantly" is a sentence with no information in it.

Prepare four to six stories from your actual work. Each should be flexible enough to map to two or three LPs depending on what the interviewer asks. A story about debugging a data quality issue at 2am can demonstrate Ownership, Dive Deep, and Deliver Results depending on how you frame it. One good story, multiple configurations.

Don't make up stories. Amazon interviewers ask follow-ups that will surface inconsistency immediately. They're trained for this. The follow-up to "I owned the project end-to-end" is "walk me through the specific decision you made when the pipeline started dropping events." You need an actual answer.

The LPs that come up most for data engineering roles: Ownership, Dive Deep, Deliver Results, Insist on the Highest Standards, Bias for Action.

The Bar Raiser Has Veto Power

The Bar Raiser is a senior Amazonian from outside your team, trained to hold the hiring bar above the current team average. A hiring manager who wants to hire you cannot override a Bar Raiser no. Read that again.

The round can look like any other. You won't always know which one it is. Treat every round as if it might be the Bar Raiser.

What they're evaluating: long-term trajectory, not just current competence. Can you scale into the next level? Do you defend opinions clearly and update them when challenged? Do you have gaps that matter at Amazon's scale? See our Bar Raiser breakdown for the full picture on how to handle this round.

What Gets Candidates Rejected

Treating the SQL round like an afterthought. Some candidates prep for coding, walk in expecting easy questions, and meet DENSE_RANK() for the first time in their lives. Window functions, optimization, and data modeling are requirements, not extras.

Generic LP stories. "I demonstrated Ownership by taking initiative on a project" gives the interviewer nothing to write down. Hiring committees read near-transcripts, not impressions. If there's no quote-able detail, the box stays empty.

Ignoring the AWS context. Proposing a pipeline without knowing whether you mean Kinesis or Kafka, Glue or Spark, signals that you haven't worked with production data infrastructure. This round is not abstract.

Going silent in the coding round. The data engineering coding bar is lower than the SDE bar. What trips people up isn't difficulty. It's silence.

Weak data modeling. Many candidates can write a JOIN but struggle to design the schema the JOIN is running against. Those are different skills.

How to Prep Without Wasting Time

Currently active as a data engineer (3-4 weeks):

Week 1: SQL. Window functions, aggregations, optimization, data modeling. Do 30-40 problems on a platform with real query execution.
Week 2: Python coding. 20-25 LeetCode mediums in the patterns that appear most: arrays, hash maps, graphs. Time yourself.
Week 3: System design. Design three to four data pipelines from scratch. Get specific on AWS services and practice talking for 45 minutes without running out of things to say.
Week 4: Behavioral. Write six STAR stories. Map each to multiple LPs. Say them out loud. Reading them is not practice.

After a gap or switching into data engineering (8-10 weeks): Add two weeks up front for SQL fundamentals and two weeks on the AWS data ecosystem before the focused block above.

The problem with LP prep is that reading stories and saying them out loud are completely different skills. Use SpaceComplexity to practice narrating your approach in real-time, including behavioral components, before the actual loop. The gap between "I know what I want to say" and "I can say it under pressure" is where prep happens.

For how the process compares across Amazon roles, see our Amazon software engineer interview guide. For coding prep without over-indexing on patterns that don't appear in data engineering, see DSA for backend engineers.