Amazon Data Engineer Interview Questions
Roughly half of the Amazon data engineer interview isn’t technical, and that’s where most rejections happen.
Candidates with clean SQL, solid modeling, and the right stack on their resume get turned down regularly. The pattern is consistent: they couldn’t tell a specific story about diving into a data quality problem, owning a broken pipeline overnight, or pushing back on a stakeholder when the data wasn’t ready.
Amazon evaluates its Leadership Principles in every round, including the technical ones. Prepare only SQL and system design and you’ve studied for half the test.
Key Points
- Leadership Principles are scored in every round, not just a behavioral one.
- Data modeling is the hardest technical filter expect to design a schema from scratch.
- The Bar Raiser comes from outside the hiring team and carries veto authority.
- You need roughly 10 -15 prepared stories, not five.
- The technical bar is medium-hard, not algorithm-heavy; the behavioral bar is where people fall.
Quick summary: The loop runs recruiter screen, sometimes an online assessment, a technical phone screen, then three to five onsite rounds covering SQL, data modeling, ETL architecture, and coding with behavioral questions layered into all of them.
Key takeaway: Your story bank matters as much as your SQL. Vague STAR answers without numbers sink candidates as reliably as a botched schema design.
Quick promise: This guide covers each round, what the data modeling filter actually asks, which Leadership Principles matter for data engineering, how the Bar Raiser works, how Amazon differs from Meta, and a four-week plan.
A note on sourcing: interview processes change and vary by team and level. This reflects candidate reports as of 2026 – confirm specifics with your recruiter.
The Loop
1. Stages and timeline
Most candidates report three to six weeks from recruiter screen to decision:
Recruiter screen (30 min). Background, motivation, level alignment. Amazon recruiters are notably direct about compensation and leveling, so come with a researched number.
Online assessment (sometimes). Some teams use one before the phone screen; others skip it. Typically SQL plus a work-style component.
Technical phone screen (45 – 60 min). SQL, Python, and ETL design, plus at least one behavioral question. You’ll be asked to explain tradeoffs, not just produce correct output.
Onsite loop (3 – 5 rounds). Data modeling, ETL and architecture, SQL and coding, and a Bar Raiser. Leadership Principles appear throughout.
2. Four things that make Amazon structurally different
Leadership Principles run through everything. There are 16, but only seven or eight surface regularly for data engineering roles. Dive Deep and Ownership come up most.
The Bar Raiser has veto power. A trained interviewer from a different organization with no incentive to fill the role. A strong objection from them can override an enthusiastic panel.
Interviewers write feedback independently. Each submits notes before seeing anyone else’s, then debriefs. One round with thin behavioral examples drags down the whole scorecard, because there’s no opportunity for a strong round to quietly compensate.
You can run parallel loops. Amazon permits interviewing with several teams simultaneously. That improves your odds of a fit and lets you compare orgs worth asking your recruiter about.
Also worth knowing: technical focus varies by hiring team. A retail analytics org and an AWS service team will emphasize different things.
The Technical Rounds
3. SQL
Expect complex joins, CTEs, window functions, aggregation, and query optimization. The difficulty is medium to hard, harder than analyst-level SQL, less exotic than a puzzle.
What separates candidates isn’t correctness. It’s whether you narrate the tradeoffs: why this join type, what the query does at scale, where the index matters, how you’d handle nulls and duplicates. Amazon consistently weights explaining why over syntax perfection.
4. Data modeling – the real filter
Multiple candidate accounts point to the same conclusion: data modeling is where the technical loop actually filters.
You’ll be asked to design a schema from scratch for an ambiguous business scenario, then defend it through a long chain of follow-ups. Classic dimensional modeling is the vocabulary – fact and dimension tables, star versus snowflake schemas, normalization tradeoffs, slowly changing dimensions, grain.
Preparation advice from recent candidates is consistently the same: work through Kimball fundamentals until the patterns are automatic.
How to handle the round:
- Clarify the business question first. What decisions does this model support? Jumping to tables without asking is the most common mistake.
- State the grain explicitly. One row equals what, exactly?
- Justify denormalization. Say what you’re trading and why.
- Anticipate change. How does this handle a dimension attribute that changes next year?
- Expect follow-ups to attack your design. That’s the round working as intended, not a sign you’re failing.
5. ETL and architecture
Largely about assembling AWS services into a coherent pipeline; ingestion, storage, processing, orchestration, serving plus the reliability questions underneath.
Be fluent in the ecosystem: S3, Glue, Redshift, EMR, Lambda, Kinesis, Step Functions, Athena. You don’t need deep expertise in each, but you should be able to explain why you’d choose one over another and what it costs.
The judgment questions matter more than service names: what happens when a job fails halfway, how you make a pipeline rerunnable, how you handle late-arriving data, and how you’d catch a silent quality problem. If you can explain a pipeline clearly end to end, this round is winnable.
6. Python coding
Standard and practical. Data manipulation, dictionaries, sets, string processing, file handling the same family of problems as Meta’s Python screen, and not as algorithm-heavy as a software engineering loop.
Python is the usual choice, though Java and Scala are accepted. Write clean, readable code and talk through it as you go.
The Leadership Principles
This is the section to spend your time on, because it’s where the rejections cluster.
7. Which ones matter for data engineering
All 16 exist, but a subset dominates DE loops:
- Dive Deep – the most tested. Audit, investigate, get to root cause rather than accepting a surface explanation.
- Ownership – you fixed something that wasn’t strictly your responsibility.
- Customer Obsession – for data engineers, “customer” usually means the analysts and business teams consuming your tables.
- Bias for Action – you moved with incomplete information.
- Deliver Results – you shipped under constraints.
- Insist on the Highest Standards – you refused to ship something that wasn’t right.
- Earn Trust – you handled disagreement or admitted a mistake.
- Invent and Simplify – you removed complexity rather than adding it.
8. How they’re scored inside technical rounds
This is the part candidates miss. Your SQL round isn’t only a SQL round. While you write the query, the interviewer is also assessing Dive Deep, Ownership, and Customer Obsession based on how you reason.
Which means the technical rounds reward things that feel tangential: asking who consumes this data and why, mentioning what you’d verify before trusting a result, noting the failure mode you’d guard against. Those aren’t digressions. They’re scoring events.
9. The story bank math
Four to five rounds, each covering two or three Leadership Principles, with follow-up questions going deep on each. That arithmetic means five stories is not enough plan for ten to fifteen distinct situations covering at least eight principles.
They can overlap in source. One complex project can yield a Dive Deep story, an Earn Trust story, and a Deliver Results story, as long as each is a genuinely different situation with a different lesson. What you can’t do is tell the same story twice to different interviewers, because they compare notes.
10. Weak versus strong
The single most common failure is a STAR answer with no numbers.
Weak: “We had a data quality issue, so I investigated and fixed it. The stakeholders were happy afterward.”
Strong: “Our revenue table was under-reporting by about 4%. I traced it through three transformations and found a join dropping records where the customer ID had trailing whitespace from one source system. I fixed the normalization, backfilled six months, and added a reconciliation check that compares daily totals against the source. It caught two similar issues in the following quarter.”
Same event. The second has a number, a specific root cause, an action, and a durable outcome. It also demonstrates Dive Deep and Ownership without naming either.
Write your stories out. Practice them aloud. Quantify everything you can and if a project is worth telling but has no numbers attached, that’s a signal to go find them before your loop.
The Bar Raiser
One round is typically pure behavioral, run by a trained interviewer from a different organization whose job is protecting Amazon’s hiring bar rather than filling a seat.
Expect three or four Leadership Principle questions with deep follow-ups on each they’ll keep pulling on a thread to see whether your story holds up under scrutiny. Vague or rehearsed-sounding answers collapse under that pressure, which is exactly the design.
Candidates consistently describe this as the hardest conversation in the loop, and it’s the one most under-prepared. Treat it as the main event, not a formality after the technical rounds.
Amazon vs. Meta
If you’re interviewing at both, the preparation differs meaningfully.
| Amazon | Meta | |
|---|---|---|
| Screen format | SQL, Python, ETL design plus behavioral | Reported 5 SQL + 5 Python in one hour |
| Dominant filter | Leadership Principles and data modeling | Product sense and dimensional modeling |
| Behavioral weight | Very high scored in every round | Present, but less pervasive |
| Coding emphasis | Practical Python, moderate | Practical Python, speed-focused |
| Cloud knowledge | AWS ecosystem expected | Internal stack, named not mastered |
| Unique element | Bar Raiser with veto | Blended product-to-pipeline rounds |
| Org context | Varies widely by team | Product Analytics |
The short version: Amazon asks whether you own problems; Meta asks whether you understand products. Both ask whether you can model data properly.
A Four-Week Plan
Week 1 – Stories. Write ten to fifteen STAR stories before touching SQL. This is counterintuitive and it’s the right order, because finding the numbers takes longer than you expect and it’s the highest-value work.
Week 2 – SQL and modeling. Daily SQL with window functions and CTEs. Work through dimensional modeling properly grain, SCDs, star schemas and practice designing schemas from ambiguous prompts out loud.
Week 3 – Architecture and Python. AWS service selection and pipeline design, timed at 45 minutes. Daily Python data manipulation. Practice the reliability questions: failures, reruns, late data, silent errors.
Week 4 – Integration. Full mock rounds where you solve a technical problem and answer behavioral follow-ups in the same session. That switching is what the real loop feels like, and practicing the halves separately doesn’t prepare you for it.
Essential Terms
- Leadership Principles: Amazon’s 16 stated cultural principles, used as an evaluation rubric.
- Bar Raiser: A trained interviewer from outside the hiring team with veto authority.
- STAR: Situation, Task, Action, Result; the expected structure for behavioral answers.
- Grain: What a single row in a fact table represents.
- Slowly changing dimension: A pattern for handling attributes that change over time.
- Star schema: A central fact table surrounded by dimension tables.
- Debrief: The post-loop meeting where interviewers compare independent feedback.
- Parallel loop: Interviewing with multiple Amazon teams simultaneously.
Final Thoughts
The mistake that costs offers at Amazon is treating the behavioral component as the soft part you’ll handle by being personable on the day. It’s the most heavily weighted, most systematically evaluated part of the loop, and it’s scored by people specifically trained to probe past rehearsed answers.
So invert your preparation. Start with your stories, find the numbers, and practice them until the details are precise. Then drill SQL and modeling, which are conventional and improve predictably with practice.
The technical bar at Amazon is demanding but reachable. The behavioral bar is where prepared candidates separate from unprepared ones, and it’s the half you can control most directly.
Frequently Asked Questions
How hard is the Amazon data engineer interview?
Technically medium to hard deep SQL, dimensional modeling, and pipeline architecture, but not algorithm-intensive. The overall difficulty comes from breadth and from the behavioral weighting rather than from any single technical question.
How many Leadership Principle stories do I need?
Plan for ten to fifteen distinct situations covering at least eight principles. With four to five rounds each probing two or three principles, a smaller bank runs out and repeating a story across interviewers is noticed at debrief.
What is the Bar Raiser looking for?
Whether hiring you raises Amazon’s average. They’re typically outside your prospective team, focused on Leadership Principles and judgment, and they probe deeply enough to expose stories that aren’t genuinely yours.
Do I need deep AWS expertise?
Working familiarity rather than certification-level depth. You should be able to design a pipeline using S3, Glue, Redshift, EMR, Lambda, and orchestration, and explain why you chose each piece. The judgment matters more than the feature lists.
Can I use Python for the coding rounds?
Yes, and it’s the usual choice. Java and Scala are accepted. Questions center on practical data manipulation rather than algorithms.
How long does the process take?
Commonly three to six weeks from recruiter screen to decision, depending on scheduling, whether an online assessment is included, and whether you’re running parallel loops.
Should I interview with multiple teams at once?
It’s permitted and often sensible. It increases your chance of landing with a team that fits and lets you compare orgs, since scope and focus vary considerably across Amazon.
What’s the most common reason candidates get rejected?
Weak behavioral stories vague STAR answers without quantified results. Insufficient SQL depth and poor data modeling follow. Notably, the top reason is the one most candidates spend the least time preparing.
P.S. Do this before anything else. Open a document and write down every project from the last three years where something went wrong and you fixed it. For each one, find the actual number records affected, hours saved, percentage off, days of delay. Most people discover they have six or seven real stories and no numbers attached to any of them. Finding those numbers takes a week and it’s the highest-return week of Amazon preparation you’ll spend, because every round in the loop is scored partly on exactly that.
