Amazon Data Engineer Interview Questions (2026)
The 15 Data Engineer interview questions most worth practising for Amazon, selected from a bank of 72. Design and operate scalable data pipelines and platforms powering analytics and ML. Below: the interview process, the questions with answer outlines, the topics tested, and how to prepare.
Every round pairs technical evaluation with Leadership Principle probing in strict STAR format, and a trained Bar Raiser from outside the hiring team holds veto power to keep the bar rising; India (Bangalore/Hyderabad/Chennai) runs the exact same LP bar as the US.
Questions
15
from a 72-question bank
Difficulty
Medium
from our question mix
Rounds
6
typical loop
Amazon rating
3.91/5
Top 99% in Internet
Amazon's interview process
- 1Online Assessment (SDE OA)60 minMedium
Two timed coding problems plus a workplace-simulation and logic section; the main gate for freshers and India volume hiring.
- 2Phone screen45 minMedium
One coding problem plus 1-2 Leadership Principle STAR questions with an SDE.
- 3Coding loop round60 minMedium
DSA problem to working code, followed by assigned-LP behavioral questions in STAR format.
- 4System design loop round60 minHard
Design an Amazon-scale service with capacity math, plus LPs; low-level/OOD design substitutes for junior candidates.
- 5Hiring Manager round45 minMedium
Team fit, project deep dives, and Deliver Results/Bias for Action stories with the manager you would report to.
- 6Bar Raiser60 minHard
An interviewer from outside the team stress-tests LP stories and overall bar with the hardest cross-examination of the loop; holds veto.
Data Engineer interview questions for the Amazon loop
- Q1
Design an event contract and schema registry process for Amazon's order and fulfillment event stream producers and data consumers
MediumRound 6: System DesignData ContractsHow to answer:Define versioned schemas, compatibility rules, ownership, validation at ingestion, documentation, and a migration process for breaking changes.
- Q2
Design a star schema for Amazon's e-commerce and logistics platform that supports reporting on fulfilled orders, gross merchandise sales, and browse -> add to cart -> order -> ship -> deliver
MediumRound 4: Data ModelingDimensional ModelingHow to answer:Define a clear fact grain for orders, add conformed dimensions such as product, seller, customer, and fulfillment dimensions, and store additive measures separately from derived metrics.
- Q3
Design reconciliation logic for Amazon's ETL so gross merchandise sales in commerce data warehouse matches the operational source
MediumRound 5: ETL DesignETL ReconciliationHow to answer:Compare control totals by date and fulfillment region, track accepted tolerances, investigate deltas, and block publishing on material mismatches.
- Q4
For Amazon's privacy-sensitive data such as shipping address, tell me about a time you raised an ethics, privacy, or governance concern
HardRound 8: BehavioralEthics and PrivacyHow to answer:Describe the concern, policy or risk, who you involved, the decision, and how the safer approach still met business needs.
- Q5
How would you model Amazon's Prime delivery promise experiment results so analysts can compare treatment and control without metric leakage?
HardRound 4: Data ModelingExperimentation ModelingHow to answer:Create assignment facts at exposure time, immutable variant dimensions, and outcome facts joined by actor and valid time windows.
- Q6
Design a data platform feature store or serving layer for Amazon's dashboards and downstream ML features using order, shipment, inventory, and browse events
HardRound 6: System DesignFeature/Data ServingHow to answer:Define feature contracts, compute batch and streaming features, store point-in-time-correct values, monitor drift, and control access.
- Q7
For Amazon's orders data, design an incremental load using CDC or high-watermark logic
HardRound 5: ETL DesignIncremental ETLHow to answer:Capture changes since the last checkpoint, deduplicate, handle deletes and updates, merge into curated tables, and persist checkpoints transactionally.
- Q8
At Amazon, describe a time you disagreed with analysts, PMs, or ML teams about the definition of fulfilled orders. How did you resolve it?
MediumRound 7: Hiring ManagerInfluence and AlignmentHow to answer:Align on business intent, document the definition, compare examples, get decision-maker approval, and publish a certified metric.
- Q9
Design a lakehouse or warehouse architecture for Amazon's billions of records per day of order, shipment, inventory, and browse events
HardRound 6: System DesignLakehouse/Warehouse DesignHow to answer:Separate raw, cleaned, curated, and serving layers; choose partitioned open formats or warehouse tables; enforce governance and cost controls.
- Q10
Describe a time you learned a new technology quickly, such as a tool in Amazon's Kinesis, Spark/EMR, Redshift, Glue, and Airflow-like orchestration, to deliver a data engineering project
EasyRound 8: BehavioralLearning AgilityHow to answer:Explain why the tool was needed, how you learned it, how you reduced risk, and what you delivered.
- Q11
How would you add lineage and auditability to Amazon's orders ETL pipeline?
MediumRound 5: ETL DesignLineage and AuditHow to answer:Capture source version, run ID, code version, input/output counts, checksums, timestamps, and upstream/downstream table dependencies.
- Q12
Tell me about a Amazon-relevant data platform project where you improved reliable commerce and fulfillment analytics. What was your impact?
MediumRound 7: Hiring ManagerProject Deep DiveHow to answer:Use STAR: context, ownership, technical actions, quantified impact, and what you learned.
- Q13
A Amazon PySpark job works in dev but fails with OOM in production. Walk through your debugging process
HardRound 3: PySparkPySpark DebuggingHow to answer:Inspect Spark UI stages, input sizes, skew, spills, executor memory, joins, caching, and partitions; then change code or configuration based on evidence.
- Q14
Design a PySpark incremental upsert for Amazon's partitioned order-event data lake where late orders updates can arrive for the last 7 days
HardRound 3: PySparkPySpark Incremental LoadsHow to answer:Read only changed partitions, deduplicate updates, MERGE on business key, update changed columns, insert new rows, and track processed checkpoints.
- Q15
A PySpark job for Amazon's order_events is slow because one fulfillment region has most of the traffic. How would you fix the skew?
HardRound 3: PySparkPySpark Skew HandlingHow to answer:Detect skewed keys, use salting for heavy keys, pre-aggregate where possible, tune shuffle partitions, and avoid exploding data unnecessarily.
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Topics tested most
How to prepare for the Amazon Data Engineer interview
Prepare 8-12 STAR stories mapped to Leadership Principles; expect a Bar Raiser; quantify impact
Indicative Data Engineer pay in India: ~₹10–45 LPA (role-level range, not a Amazon-specific figure).
Frequently asked questions
How hard is the Amazon Data Engineer interview?
Based on our 72-question Data Engineer bank for the Amazon loop, the overall difficulty is medium (Amazon's process is generally rated elevated). Expect around 6 rounds spanning Accountability, Ambiguity, Conflict Resolution.
How many interview rounds does Amazon have for a Data Engineer?
Amazon typically runs about 6 rounds for Data Engineer candidates: Online Assessment (SDE OA) → Phone screen → Coding loop round → System design loop round → Hiring Manager round.
What is the interview process at Amazon?
The Amazon interview process typically runs: Online assessment -> phone screen -> 4-5 'loop' rounds, each mapped to Leadership Principles, with a Bar Raiser. Prepare for each round in order rather than only the first — the later stages usually carry the most weight.
How hard is the Amazon interview?
Amazon interviews are rated high difficulty. The bar is highest on leadership principles (behavioral) — go deep there and practise explaining your reasoning out loud.
What does Amazon look for in candidates?
Amazon focuses on Leadership Principles (behavioral), coding, system design, ownership. Culturally, it values 16 Leadership Principles: customer obsession, ownership, dive deep, bias for action. Line up your examples to hit both the technical bar and these values.
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Compiled by PrepNPlaced from 72+ interview reports and question banks for the Amazon Data Engineer loop, cross-referenced with 32,782 employee reviews. Data refreshed 2026-08-13. Updated 2026.