Amazon's data-engineer and BIE interviews in India combine advanced SQL, AWS-native ETL, and operational rigour, all wrapped in the Leadership Principles.
In 2026 expect a SQL round with warehouse-aware optimisation (retention cohorts on Redshift, distribution and sort keys, columnar scans), an ETL-design round on loading transactional data with schema-drift and idempotent reloads using Glue, S3, EMR, and Redshift, and a data-quality round on catching broken pipelines before dashboards. Interviewers weight correctness, cost-awareness, and clear pipeline SLAs, and every round is scored against Ownership, Dive Deep, and Insist on the Highest Standards. Bring STAR stories alongside technical depth.
About Amazon
Largest e-commerce + AWS cloud + Alexa + Prime Video + Ring.
Online assessment with SQL and data problems
Technical phone screen on SQL and ETL
Onsite loop: SQL, data modelling, pipeline design, and Leadership Principles
Debrief and offer
Round 1 (60-90 min)
online assessment with SQL and data-manipulation problems.
Round 2 (60 min)
advanced SQL and warehouse-optimisation round.
Round 3 (60 min)
ETL and pipeline design round on AWS with schema-drift and idempotency.
Round 4 (45-60 min)
data-quality and Leadership-Principles behavioural round.
Sourced from 2+ candidate post-mortems. Hit Practice to answer any one with AI voice feedback.
The typical Amazon recruitment process has 4 stages: Online assessment with SQL and data problems → Technical phone screen on SQL and ETL → Onsite loop: SQL, data modelling, pipeline design, and Leadership Principles → Debrief and offer.
Amazon typically conducts 4 interview rounds: Round 1 (60-90 min): online assessment with SQL and data-manipulation problems.; Round 2 (60 min): advanced SQL and warehouse-optimisation round.; Round 3 (60 min): ETL and pipeline design round on AWS with schema-drift and idempotency.; Round 4 (45-60 min): data-quality and Leadership-Principles behavioural round..
HireStepX recommends the Optimise-and-Own framework for this type of interview: Write warehouse-aware SQL, design idempotent AWS-native ETL with data-quality gates, and back every choice with cost, SLA, and Leadership-Principle reasoning
To answer this question well, HireStepX recommends the Optimise-and-Own approach: Write warehouse-aware SQL, design idempotent AWS-native ETL with data-quality gates, and back every choice with cost, SLA, and Leadership-Principle reasoning Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Optimise-and-Own approach: Write warehouse-aware SQL, design idempotent AWS-native ETL with data-quality gates, and back every choice with cost, SLA, and Leadership-Principle reasoning Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Optimise-and-Own approach: Write warehouse-aware SQL, design idempotent AWS-native ETL with data-quality gates, and back every choice with cost, SLA, and Leadership-Principle reasoning Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Optimise-and-Own approach: Write warehouse-aware SQL, design idempotent AWS-native ETL with data-quality gates, and back every choice with cost, SLA, and Leadership-Principle reasoning Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Optimise-and-Own approach: Write warehouse-aware SQL, design idempotent AWS-native ETL with data-quality gates, and back every choice with cost, SLA, and Leadership-Principle reasoning Ground your answer in a specific real example from your own experience.