Amazon
Data engineer loop
Reported data engineer interview patterns at Amazon, distilled for prep mapping against the right-hand column.
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
Reported data engineer interview patterns at Amazon, distilled for prep mapping against the right-hand column.
Data engineer loop
Reported data engineer interview patterns at Google, distilled for prep mapping against the right-hand column.
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.
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