Amazon Big Data Engineer Interview Questions (2026)
The 15 Big Data Engineer interview questions most worth practising for Amazon, selected from a bank of 72. Build distributed pipelines that ingest, process, and store terabytes of data reliably at scale. 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.
Big Data Engineer interview questions for the Amazon loop
- Q1
At Amazon, data decisions often involve trade-offs. Tell me about a conflict with another engineer over commerce data warehouse or Kinesis, Spark/EMR, Redshift, Glue, and Airflow-like orchestration
MediumRound 8: BehavioralConflict ResolutionHow to answer:State both positions fairly, explain evidence gathered, describe the decision process, and show the relationship stayed healthy.
- Q2
Design cost controls for Amazon's commerce data warehouse where query and pipeline spend is growing faster than usage
MediumRound 6: System DesignCost and Performance DesignHow to answer:Measure cost by owner and workload, optimize scans and files, right-size compute, cache or materialize common aggregates, and enforce budgets.
- Q3
Define data quality checks for Amazon's order_id, customer_id, seller_id, sku, status, amount, event_time pipeline before publishing to analysts
MediumRound 5: ETL DesignData QualityHow to answer:Check schema, nullability, uniqueness, referential integrity, volume anomalies, value ranges, freshness, and reconciliation against source totals.
- Q4
Design a Amazon data system for near-real-time order and inventory analytics with end-to-end latency of under 5 minutes
HardRound 6: System DesignData System DesignHow to answer:Use durable event ingestion, streaming processing, curated storage, low-latency serving, monitoring, and replayable raw logs.
- Q5
Design a daily ETL pipeline for Amazon that ingests order and fulfillment event stream into commerce data warehouse for fulfilled orders reporting
MediumRound 5: ETL DesignETL ArchitectureHow to answer:Land raw data, validate schema, transform to curated tables, run data quality checks, publish aggregates, and monitor freshness and failures.
- Q6
How would you make Amazon's Airflow DAG for orders processing idempotent and safe to backfill?
HardRound 5: ETL DesignETL OrchestrationHow to answer:Use deterministic input ranges, write to temporary paths, validate outputs, atomic swap/merge, and parameterize DAG runs by logical date.
- Q7
For Amazon, decide between ETL and ELT for transforming order, shipment, inventory, and browse events. What factors drive your choice?
MediumRound 5: ETL DesignETL vs ELTHow to answer:Choose ELT when the warehouse/lakehouse can scale transformations cheaply; choose ETL when privacy, bandwidth, or source constraints require pre-load shaping.
- Q8
For Amazon, would you model order, shipment, inventory, and browse events as an event fact table, an accumulating snapshot, or a periodic snapshot? Defend the choice
HardRound 4: Data ModelingFact Table DesignHow to answer:Use event facts for immutable actions, accumulating snapshots for lifecycle progress, and periodic snapshots for state at regular intervals.
- Q9
Design access control for Amazon's analytics platform where shipping address must be protected but aggregated analysis is allowed
HardRound 6: System DesignGovernance and SecurityHow to answer:Use role-based and attribute-based controls, row/column masking, tokenization, governed joins, audit logs, and least-privilege access.
- Q10
Walk me through a production incident in a data pipeline similar to Amazon's orders platform
HardRound 7: Hiring ManagerIncident LeadershipHow to answer:Explain detection, triage, root cause, mitigation, communication, and prevention with concrete metrics.
- Q11
In Amazon's e-commerce and logistics platform, design a bridge table for many-to-many relationships between customers and sellers
HardRound 4: Data ModelingMany-to-Many ModelingHow to answer:Create a bridge table with surrogate relationship keys, effective dates when needed, allocation weights if measures must be split, and referential integrity checks.
- Q12
Why Amazon, and how does your experience map to customer obsession, ownership, dive deep, and bias for action?
EasyRound 7: Hiring ManagerMotivation and FitHow to answer:Connect specific company problems to your past work, show motivation, and give examples that demonstrate the stated values.
- Q13
For Amazon, tell me about a time you took ownership of a failing data pipeline that affected customers or business users like customers
MediumRound 8: BehavioralOwnershipHow to answer:Use STAR, describe the failure, your ownership, cross-team actions, impact, and prevention.
- Q14
Choose partitioning and clustering keys for Amazon's fact_orders to support common queries by time and fulfillment region
MediumRound 4: Data ModelingPhysical Data ModelingHow to answer:Partition primarily by event date, cluster or sort by fulfillment region and high-value join/filter keys, and avoid high-cardinality partitions.
- Q15
How would you prioritize between reducing Amazon's pipeline cost, improving freshness, and adding a new gross merchandise sales feature?
MediumRound 7: Hiring ManagerPrioritizationHow to answer:Estimate business value, risk, user impact, effort, and reversibility; align stakeholders on a ranked roadmap.
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Topics tested most
How to prepare for the Amazon Big Data Engineer interview
Prepare 8-12 STAR stories mapped to Leadership Principles; expect a Bar Raiser; quantify impact
Indicative Big Data Engineer pay in India: ~₹8–35 LPA (role-level range, not a Amazon-specific figure).
Frequently asked questions
How hard is the Amazon Big Data Engineer interview?
Based on our 72-question Big 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 Big Data Engineer?
Amazon typically runs about 6 rounds for Big 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 Big Data Engineer loop, cross-referenced with 32,782 employee reviews. Data refreshed 2026-08-13. Updated 2026.