Comparison
Amazon vs Google data engineering interviews compare AWS-style ownership with Google's broad data systems bar. Candidates should expect SQL, pipelines, distributed systems and a clear difference in behavioural weighting.
Data engineer loop
Amazon frames data engineering as service ownership: pipelines built from AWS primitives, operational load handled with runbooks and costs treated as a design input. Interviews mix SQL, data design and heavy Leadership Principles coverage, with the Bar Raiser present as always. Candidates who have run production data systems and can narrate the failures convert well.
Data engineer loop
Google's data engineering interviews test whether your fundamentals generalise: analytical SQL, batch and streaming design, schema evolution and the distributed-systems reasoning underneath it all. The company's internal data tooling is famous, but interviews stay tool-agnostic and concept-driven. Committee review closes the process, keeping level decisions consistent across the organisation.
Candidate-reported patterns vary by team and quarter. Use this as a prep map, then confirm current details with your recruiter.
| Dimension | Amazon | |
|---|---|---|
| Interview rounds | Screen or assessment, data design, coding or SQL, behavioural and Bar Raiser. | Technical screen, SQL or coding, data systems design, Googleyness and committee. |
| Data systems depth | AWS data services, streaming, warehousing, operational ownership and cost. | BigQuery-style analytics, distributed storage, pipelines and reliability at global scale. |
| SQL depth | Practical joins, windows, aggregation and data quality questions. | SQL plus algorithmic reasoning and performance tradeoffs. |
| Pipeline design | Kinesis, Glue, Redshift, S3 and service ownership patterns are natural examples. | Batch and streaming systems, schema evolution, freshness and backfill strategy. |
| Behavioural framework | Leadership Principles appear in nearly every round. | Googleyness, collaboration and ambiguity handling. |
| Take-home | Rare for standard loops. | Rare for standard loops. |
| Offer typical TC | High Big Tech TC, with level and vesting shape important. | High Big Tech TC, with committee-controlled level calibration. |
| Decision speed | Often fast once Bar Raiser aligns. | Can be slower due to committee and team match. |
Amazon rewards concrete experience with cloud primitives, operational load and cost-aware design.
LP answers map well to pipeline incidents, data quality failures and stakeholder pressure.
AWS and retail data platforms offer many production data surfaces.
Google's data engineering loops generalise across analytics, ML data, Search, Ads and Cloud.
Algorithms, distributed systems and SQL remain a stable prep path.
Google still tests collaboration, but Amazon makes behavioural evidence central.
How we build these comparisons: each one draws on public candidate reports and the hiring pages both companies publish, then gets an editorial pass for balance. Processes change by team and quarter, so confirm specifics with your recruiter.
Last reviewed by the site editor: June 2026