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Big Tech & Enterprise Software

Amazon

E-commerce / cloud / advertising · Data Engineering interview

AWS-native, SQL + data-modeling heavy, with Leadership Principles woven through every round and a Bar Raiser.

Cloud / platform

AWS-native: Redshift, S3, EMR, Glue, Athena, Kinesis

Reported difficulty

~3.5 / 5

SQLPythonSparkRedshiftGlueAirflow

Hiring process

Recruiter

HM screen (1 LP)

Online Assessment

Onsite loop (3–5)

Bar Raiser / Behavioral

Offer

Round-by-round breakdown

Online Assessment

· HackerRank-styleHigh confidence

1 medium Python question
1 advanced SQL question
15 SQL MCQs (identify the incorrect query)
Behavioral scenarios aligned to Leadership Principles

Written SQL (variant)

· paper, no IDEMedium confidence

~10 situational SQL questions against a provided data model
2 basic Python questions

Data Modeling

High confidence

Star vs snowflake schema
Normal forms; SCD types
Design a lending data model
OLTP vs OLAP; row vs columnar storage

System Design

High confidence

Batch pipeline design
Data sharding & consistent hashing
Schema selection + justification

Behavioral (LP / Bar Raiser)

High confidence

"A project you are proud of — why?"
"A decision that caused failure — what did you learn?"
Dive Deep, Ownership, Deliver Results, Invent & Simplify

System & pipeline design prompts

Design a data lake
Batch pipeline for a given data model
Data sharding & consistent hashing
Streaming ingestion at scale

Prep — high priority

SQL: window functions, ranking, rolling averages, dedup
Data modeling: star/snowflake, SCD types, normal forms, Redshift keys
8–10 reusable STAR stories, each mapped to several Leadership Principles

Prep — medium priority

Spark internals; Python ETL (idempotency, late data)
AWS services (Redshift/S3/EMR/Glue/Kinesis)
Sharding & consistent hashing

Rejection patterns

Weak/under-prepared Leadership Principle stories (the Bar Raiser shares the final hiring decision)
Shaky data modeling — wrong SCD or unjustified schema
Slow SQL under time pressure

Reported experiences

Data Engineer

Not statedFirst-hand

6 rounds: OA (Python + advanced SQL + 15 SQL MCQs + LP) → in-person written SQL (10 situational on a data model) → fundamentals (star vs snowflake, Spark, SCD, Kafka, lending model) → system design (sharding, OLTP vs OLAP) → tech+behavioral → final behavioral.

Data Engineer

2025First-hand

TeamBlind "My Amazon Data Engineer Interview Experience (2025)" — confirms the recruiter → OA → loop → Bar Raiser shape (body was login-walled).

Rehearse for Amazon

Answered questions on what Amazon actually asks — each with the short answer, the answer that gets you rejected, and the follow-up.

Then build one and have it reviewed: design a daily orders pipeline or model a marketplace. No account.

All interview experiences

Openingloading the content…

Big Tech & Enterprise Software

Amazon

E-commerce / cloud / advertising · Data Engineering interview

AWS-native, SQL + data-modeling heavy, with Leadership Principles woven through every round and a Bar Raiser.

Cloud / platform

AWS-native: Redshift, S3, EMR, Glue, Athena, Kinesis

Reported difficulty

~3.5 / 5

SQLPythonSparkRedshiftGlueAirflow

Hiring process

Recruiter

HM screen (1 LP)

Online Assessment

Onsite loop (3–5)

Bar Raiser / Behavioral

Offer

Round-by-round breakdown

Online Assessment

· HackerRank-styleHigh confidence

1 medium Python question
1 advanced SQL question
15 SQL MCQs (identify the incorrect query)
Behavioral scenarios aligned to Leadership Principles

Written SQL (variant)

· paper, no IDEMedium confidence

~10 situational SQL questions against a provided data model
2 basic Python questions

Data Modeling

High confidence

Star vs snowflake schema
Normal forms; SCD types
Design a lending data model
OLTP vs OLAP; row vs columnar storage

System Design

High confidence

Batch pipeline design
Data sharding & consistent hashing
Schema selection + justification

Behavioral (LP / Bar Raiser)

High confidence

"A project you are proud of — why?"
"A decision that caused failure — what did you learn?"
Dive Deep, Ownership, Deliver Results, Invent & Simplify

System & pipeline design prompts

Design a data lake
Batch pipeline for a given data model
Data sharding & consistent hashing
Streaming ingestion at scale

Prep — high priority

SQL: window functions, ranking, rolling averages, dedup
Data modeling: star/snowflake, SCD types, normal forms, Redshift keys
8–10 reusable STAR stories, each mapped to several Leadership Principles

Prep — medium priority

Spark internals; Python ETL (idempotency, late data)
AWS services (Redshift/S3/EMR/Glue/Kinesis)
Sharding & consistent hashing

Rejection patterns

Weak/under-prepared Leadership Principle stories (the Bar Raiser shares the final hiring decision)
Shaky data modeling — wrong SCD or unjustified schema
Slow SQL under time pressure

Reported experiences

Data Engineer

Not statedFirst-hand

Data Engineer

2025First-hand

TeamBlind "My Amazon Data Engineer Interview Experience (2025)" — confirms the recruiter → OA → loop → Bar Raiser shape (body was login-walled).

Rehearse for Amazon

Answered questions on what Amazon actually asks — each with the short answer, the answer that gets you rejected, and the follow-up.

Then build one and have it reviewed: design a daily orders pipeline or model a marketplace. No account.

All interview experiences