How many rounds are in the Amazon Data Engineer interview loop?

Most candidates go through four stages: an online assessment, one or two technical phone screens, an onsite virtual loop of 4–6 rounds, and a debrief where the bar raiser weighs in. Total elapsed time is typically 3–6 weeks from recruiter contact to offer.

What SQL topics does Amazon test for Data Engineers?

Amazon focuses on intermediate-to-advanced SQL: window functions (ROW_NUMBER, RANK, DENSE_RANK, LAG/LEAD), CTEs, multi-table joins, rolling aggregations, and query optimization. Expect at least two SQL problems per loop, plus follow-ups on indexing and execution plans.

What is the bar raiser and how should I prepare for it?

The bar raiser is a trained Amazon employee from a completely different org who sits in on the loop. They focus almost entirely on Leadership Principles behavioral questions and have effective veto power over the hire. Prepare five to eight detailed STAR stories that each map to multiple Leadership Principles, and be ready to go two or three levels deep on any decision you describe.

Which Amazon Leadership Principles come up most in Data Engineer interviews?

Customer Obsession, Ownership, Dive Deep, Invent and Simplify, and Deliver Results appear most often. Data Engineers are expected to demonstrate Dive Deep in particular — interviewers want to see that you understand your data end-to-end, not just the pipeline surface.

What does the Amazon Data Engineer system design round look like?

You are given a vague business requirement (e.g., 'build a pipeline to ingest clickstream events from 50 million daily users') and asked to design the end-to-end architecture. You should discuss ingestion (Kinesis vs. Kafka), storage (S3, Redshift, DynamoDB), transformation (Spark, Glue, Flink), orchestration (Airflow), and monitoring. Justify every trade-off explicitly.

How does Amazon decide L4 vs L5 for Data Engineer offers?

L4 is roughly equivalent to an engineer with 2–4 years of experience who can own a defined component. L5 owns end-to-end initiatives and can lead design reviews. Compensation at L4 averages $143K total (base $108K + RSUs + bonus); L5 averages $199K total; L6 averages $258K total, per Levels.fyi data from early 2026.

Does Amazon ask Python or coding questions for Data Engineer roles?

Yes. Expect one to two coding problems in the loop — usually medium-difficulty LeetCode-style questions (array manipulation, hash maps, sliding windows) or ETL-flavored problems such as parsing and transforming a log file, deduplicating records, or handling late-arriving data.

How should I handle the 'tell me about a time you failed' question at Amazon?

Amazon interviewers expect genuine failures, not disguised strengths. Describe a real mistake clearly, explain what specifically went wrong in your decision-making, and give concrete details about what you changed afterward. Vague or polished-sounding failures are red flags to experienced Amazon interviewers.

Amazon Data Engineer Interview Questions (2026)

How many rounds are in the Amazon Data Engineer interview loop?: Most candidates go through four stages: an online assessment, one or two technical phone screens, an onsite virtual loop of 4–6 rounds, and a debrief where the bar raiser weighs in. Total elapsed time is typically 3–6 weeks from recruiter contact to offer.
What SQL topics does Amazon test for Data Engineers?: Amazon focuses on intermediate-to-advanced SQL: window functions (ROW_NUMBER, RANK, DENSE_RANK, LAG/LEAD), CTEs, multi-table joins, rolling aggregations, and query optimization. Expect at least two SQL problems per loop, plus follow-ups on indexing and execution plans.
What is the bar raiser and how should I prepare for it?: The bar raiser is a trained Amazon employee from a completely different org who sits in on the loop. They focus almost entirely on Leadership Principles behavioral questions and have effective veto power over the hire. Prepare five to eight detailed STAR stories that each map to multiple Leadership Principles, and be ready to go two or three levels deep on any decision you describe.
Which Amazon Leadership Principles come up most in Data Engineer interviews?: Customer Obsession, Ownership, Dive Deep, Invent and Simplify, and Deliver Results appear most often. Data Engineers are expected to demonstrate Dive Deep in particular — interviewers want to see that you understand your data end-to-end, not just the pipeline surface.
What does the Amazon Data Engineer system design round look like?: You are given a vague business requirement (e.g., 'build a pipeline to ingest clickstream events from 50 million daily users') and asked to design the end-to-end architecture. You should discuss ingestion (Kinesis vs. Kafka), storage (S3, Redshift, DynamoDB), transformation (Spark, Glue, Flink), orchestration (Airflow), and monitoring. Justify every trade-off explicitly.
How does Amazon decide L4 vs L5 for Data Engineer offers?: L4 is roughly equivalent to an engineer with 2–4 years of experience who can own a defined component. L5 owns end-to-end initiatives and can lead design reviews. Compensation at L4 averages $143K total (base $108K + RSUs + bonus); L5 averages $199K total; L6 averages $258K total, per Levels.fyi data from early 2026.
Does Amazon ask Python or coding questions for Data Engineer roles?: Yes. Expect one to two coding problems in the loop — usually medium-difficulty LeetCode-style questions (array manipulation, hash maps, sliding windows) or ETL-flavored problems such as parsing and transforming a log file, deduplicating records, or handling late-arriving data.
How should I handle the 'tell me about a time you failed' question at Amazon?: Amazon interviewers expect genuine failures, not disguised strengths. Describe a real mistake clearly, explain what specifically went wrong in your decision-making, and give concrete details about what you changed afterward. Vague or polished-sounding failures are red flags to experienced Amazon interviewers.

Amazon hires Data Engineers into a highly structured process that is unlike most tech companies. The behavioral and technical rounds are weighted equally, and the bar raiser — a veto-carrying interviewer from outside the hiring team — can overturn consensus. If you treat this as a standard SQL-plus-coding interview, you will likely stall in the debrief.

This guide walks through every stage of the loop, the specific question types by round, sample answers, compensation context by level, and a concrete four-week prep plan.

The Amazon interview loop: structure and timeline

The process from recruiter outreach to offer decision runs 3–6 weeks and follows a consistent four-stage structure.

Stage 1 — Online Assessment (OA). Sent 2–3 days after recruiter contact. Format in 2026 is typically 2–3 hours with two DSA coding problems, a work-style survey mapped to Leadership Principles, and in some tracks a code-debugging section. Data Engineer OAs sometimes include a SQL or data manipulation problem alongside the algorithmic portion. The coding problems are roughly LeetCode medium difficulty.

Stage 2 — Technical phone screen. One or two 45-minute calls with a member of the hiring team. Each screen has a single SQL or Python problem plus at least one behavioral question tied to a Leadership Principle. The SQL question will not end at “write the query” — the interviewer will ask you to optimize it, handle NULL values, or explain the execution plan.

Stage 3 — Virtual onsite loop. Four to six back-to-back 60-minute video calls. Each interviewer is assigned specific Leadership Principles they must cover, and they cannot deviate. Technical rounds include:

SQL deep dive (two problems, window functions, CTEs, ranking)
Coding / algorithms (one to two problems, Python preferred)
Data modeling (warehouse design, normalization, slowly changing dimensions)
System design / ETL architecture (end-to-end pipeline design)
Behavioral (Leadership Principles only, no technical content)

Stage 4 — Bar raiser. Embedded in the onsite loop — you usually won’t know which interviewer is the bar raiser. They focus almost entirely on Leadership Principles and can probe stories from your other rounds. Their strong objection blocks a hire even if every other interviewer votes to proceed.

Debrief happens within a week of the loop. Recruiters typically share a verbal decision before the written offer, which takes another 1–2 weeks.

What Amazon uniquely evaluates

Three things set Amazon’s Data Engineer interview apart from similar roles at Google or Meta.

Leadership Principles are not a formality. Amazon’s 16 Leadership Principles are a genuine scoring rubric. Interviewers take notes keyed to specific principles, and each one must be demonstrated with a concrete past example. Abstract answers (“I always prioritize the customer”) score zero.

Dive Deep is a technical differentiator. For Data Engineers specifically, Amazon probes whether you understand your data end-to-end — source systems, business definitions, schema evolution, and downstream consumers. Candidates who describe their pipeline surface without knowing what produces the data or who consumes it fail this dimension.

Ownership is measured at scope. Amazon distinguishes between engineers who complete assigned tasks (below the bar at L5) and engineers who identify problems that no one asked them to solve, drive a fix, and report the outcome. Every significant behavioral story should include something you noticed, not something you were told to fix.

SQL and data modeling round

Expect two SQL problems ranging from 15 to 25 minutes each. Common question patterns:

Running totals and moving averages. “Given a table of daily revenue per seller, compute the 7-day rolling average for each seller.” Tests window functions with AVG(...) OVER (PARTITION BY ... ORDER BY ... ROWS BETWEEN 6 PRECEDING AND CURRENT ROW).
Ranking and top-K. “Find the top 3 products by revenue in each category, including ties.” Tests DENSE_RANK() vs RANK() and how you handle the tie-breaking requirement.
Session reconstruction. “Given a table of page events with user_id and event_time, define a session as events within 30 minutes of each other. Count sessions per user.” Tests LAG(), date arithmetic, and conditional logic.
SCD and upserts. “Design a schema for a slowly changing dimension on a customer address. Write a query that returns the address as it was on any given date.” Tests Type 2 SCD design and range-based filtering.

For data modeling, Amazon frequently asks you to design a schema for a real-seeming internal problem — order fulfillment, delivery routing, or seller analytics. They want to see normalization decisions explained, not just an ER diagram.

System design / ETL architecture round

The prompt is intentionally open-ended. A typical example: “Our marketing team needs a daily report showing customer lifetime value by acquisition channel. Right now this is done in Excel. Design the system.”

Work through this with a structured flow:

Clarify requirements — row volume, latency SLA (batch daily, hourly, real-time?), upstream sources, downstream consumers
Ingestion — S3 event bridge, Kinesis Data Streams, or direct database replication depending on latency
Storage — raw zone in S3 (Parquet), curated zone in Redshift or Glue catalog, access patterns for BI tools
Transformation — Spark on EMR or AWS Glue for batch; Apache Flink or Kinesis Data Analytics for streaming
Orchestration — Apache Airflow for dependency management, retry logic, alerting
Reliability — idempotency (overwrite by partition, not append), dead-letter queues, schema registry for Avro/Protobuf

When discussing trade-offs, be explicit: “I chose batch over streaming here because the SLA is daily and streaming adds operational complexity without business benefit.” Amazon interviewers respond well to clear reasoning; they penalize hedging.

A question that trips up many candidates: “How do you handle late-arriving data?” A strong answer describes a watermark strategy (e.g., Flink’s event-time processing with a configurable watermark delay) or a reprocessing framework where the pipeline can be re-run idempotently for any date range.

Coding round

Amazon Data Engineer coding questions lean toward data manipulation rather than pure algorithms, though L5 loops may include a harder graph or dynamic programming problem. Representative questions:

Deduplication. Given a list of log entries with duplicate event IDs from a retry mechanism, return only the first occurrence of each event, preserving original order.
Sliding window. Given a stream of user events, find all users who triggered more than 5 events within any 10-minute window (anomaly detection pattern).
Flatten and normalize. Given a list of JSON records with nested arrays, write Python that flattens them into a relational structure suitable for loading to a database.

Time complexity matters. Explain your approach before coding, call out edge cases (empty input, duplicates, NULLs), and walk through your solution after finishing. Amazon interviewers note whether you identify edge cases proactively or only when prompted.

Behavioral rounds: Leadership Principles in practice

You will face 3–5 behavioral questions per loop, potentially from multiple interviewers. The most common patterns for Data Engineers:

Customer Obsession

“Tell me about a time you changed your technical approach based on how end users were actually using your data.”

Sample answer structure: Describe a situation where analysts were building workarounds in Excel because your pipeline output was confusing — aggregated at the wrong grain, for example. Walk through what you discovered (observing the behavior, interviewing two analysts), what you changed (adding a pre-aggregated mart layer with business-friendly column names), and the measured outcome (analyst ticket volume dropped 40% over the next quarter, based on your ticketing system data). Be specific about what you personally did versus what the team did.

Dive Deep

“Tell me about a time you discovered a data quality issue that no one else had noticed.”

Amazon expects specific technical detail: how you found it (an anomaly in a monitoring dashboard, a statistical check that flagged a distribution shift), how you traced it to the root cause (schema change upstream, a joining key with silent NULLs after a vendor migration), and what permanent fix you implemented.

Ownership

“Describe a time you took ownership of a problem outside your team’s scope.”

Weak answer: “I noticed the downstream team’s query was slow so I offered to help.” Strong answer: “I noticed the downstream team’s query was slow. I profiled it without being asked, identified a missing composite index, created a change request, worked through the change advisory process, and reduced their query latency from 45 seconds to under 3 seconds — which unblocked a weekly executive report that had been running 2 hours late.”

Invent and Simplify

“Tell me about a time you simplified a complex data process.”

Frame the before state quantitatively (pipeline had 14 interdependent steps, ran for 6 hours, failed twice a week on average), your specific simplification (consolidated into 4 idempotent stages, moved retry logic to the orchestrator rather than individual scripts), and the after state (runtime dropped to 90 minutes, failure rate near zero over 3 months).

Level and compensation context

Amazon Data Engineers are hired at L4 (entry-level SDE II equivalent), L5 (senior), and L6 (principal). The level is determined in debrief, not at application — your behavioral scope and technical depth during the loop signal where you land.

Based on Levels.fyi data from early 2026:

L4: $143K total compensation (base $108K, RSUs ~$22K/year, bonus ~$13K)
L5: $199K total (base $139K, RSUs ~$52K/year, bonus ~$8K)
L6: $258K total (base $145K, RSUs ~$112K/year, bonus near zero — Amazon caps base, loads equity at senior levels)

RSUs vest on a back-weighted schedule: 5% in year 1, 15% in year 2, 40% in year 3, 40% in year 4 — a deliberate retention mechanism that makes the second half of the vesting period significantly more valuable. Location adds roughly 10–20% in Seattle or NYC versus other US markets.

Most candidates applying externally for senior roles target L5. If you are currently a tech lead or staff engineer elsewhere, make sure your behavioral stories demonstrate the scope and ownership patterns Amazon associates with L5 — otherwise the bar raiser may down-level you even if the hiring manager wants to extend an L5 offer.

Four-week prep plan

Week 1 — SQL fundamentals. Complete 30 SQL problems on StrataScratch or DataLemur, focusing on window functions, CTEs, and aggregation patterns. Time yourself: each problem should take under 20 minutes. Review execution plans for any solution you wrote.

Week 2 — Behavioral story bank. Map your past experience to Amazon’s 16 Leadership Principles. Write out eight detailed STAR stories. Each story should include specific numbers (time saved, error rate, revenue impact) and your individual contribution separated from team contribution. Practice saying them out loud — not reading them.

Week 3 — System design. Spend 3–4 sessions designing end-to-end data architectures for common patterns: real-time analytics, batch ETL to a data warehouse, event-sourced CDC pipeline, and a slowly changing dimension model. Use the AWS service stack (Kinesis, Glue, Redshift, Airflow/MWAA). For each design, prepare trade-off justifications.

Week 4 — Mock loops. Do two full mock loops — each 4 rounds back-to-back — with a timer. Record yourself. Review the behavioral answers for vagueness (phrases like “we decided” without saying who specifically made the call are a common tell). On coding, practice talking through your approach before touching the keyboard.

One frequently missed prep step: research the specific org you are interviewing with. A team on AWS Redshift engineering will focus on storage and query optimization; Prime Video data engineering will probe large-scale ingestion for streaming metadata. Ask your recruiter which team you are interviewing with and tailor your system design examples accordingly.

What happens in the debrief

After your loop, interviewers gather (virtually) within a few days. Each interviewer scores their designated Leadership Principles with evidence from the interview. The bar raiser presents a holistic assessment. A strong vote against from the bar raiser is almost always dispositive — the hiring manager cannot simply override it.

This means any single weak round can sink an otherwise strong loop. Interviewers are specifically instructed not to let enthusiasm for one area compensate for a gap in another. If you had a rough behavioral round, you cannot recover by having done well on SQL.

If you receive a “strong no-hire” and want to reapply, Amazon’s general policy requires a 12-month cooling-off period before re-interviewing for the same level. Some teams extend offers at a lower level instead — accept or decline based on your specific situation and the team’s trajectory.

Track your prep, interview dates, follow-ups, and offers in one place. A structured job tracker prevents dropped balls across a multi-week loop process — especially when you are managing multiple companies simultaneously.