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Comparison

Amazon vs Google: Data engineer interview 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 engineerCompany comparison

Companies at a glance

Amazon

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.

Google

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.

Side-by-side interview comparison

Candidate-reported patterns vary by team and quarter. Use this as a prep map, then confirm current details with your recruiter.

DimensionAmazonGoogle
Interview roundsScreen or assessment, data design, coding or SQL, behavioural and Bar Raiser.Technical screen, SQL or coding, data systems design, Googleyness and committee.
Data systems depthAWS data services, streaming, warehousing, operational ownership and cost.BigQuery-style analytics, distributed storage, pipelines and reliability at global scale.
SQL depthPractical joins, windows, aggregation and data quality questions.SQL plus algorithmic reasoning and performance tradeoffs.
Pipeline designKinesis, Glue, Redshift, S3 and service ownership patterns are natural examples.Batch and streaming systems, schema evolution, freshness and backfill strategy.
Behavioural frameworkLeadership Principles appear in nearly every round.Googleyness, collaboration and ambiguity handling.
Take-homeRare for standard loops.Rare for standard loops.
Offer typical TCHigh Big Tech TC, with level and vesting shape important.High Big Tech TC, with committee-controlled level calibration.
Decision speedOften fast once Bar Raiser aligns.Can be slower due to committee and team match.

When to prefer which

When to prefer Amazon

  • You know AWS data services

    Amazon rewards concrete experience with cloud primitives, operational load and cost-aware design.

  • You have strong ownership stories

    LP answers map well to pipeline incidents, data quality failures and stakeholder pressure.

  • You want service-level data work

    AWS and retail data platforms offer many production data surfaces.

When to prefer Google

  • You want broader data platform signal

    Google's data engineering loops generalise across analytics, ML data, Search, Ads and Cloud.

  • You are strong at fundamentals

    Algorithms, distributed systems and SQL remain a stable prep path.

  • You prefer less behavioural repetition

    Google still tests collaboration, but Amazon makes behavioural evidence central.

Prep questions for this comparison

How should data engineers weight prep between Amazon and Google?
Weight behavioural work toward Amazon and conceptual depth toward Google. Amazon needs a rehearsed portfolio of ownership stories covering data quality incidents, cost decisions and stakeholder friction. Google needs fluency explaining pipeline design from first principles without leaning on named services. SQL drilling and data-warehouse fundamentals serve both loops identically, so do that work once.
What is the reusable core between these loops?
Pipeline architecture judgement travels intact: freshness versus completeness tradeoffs, backfill strategy, partitioning and schema change management come up in both processes. Strong SQL is assumed by both. The divergence is vocabulary: Amazon interviews welcome AWS-specific fluency as evidence of experience, while Google prefers abstractions, so practise describing the same designs in both dialects.
Which loop favours my background?
Amazon favours engineers with hands-on cloud operational history and crisp behavioural narration, and its faster decision cycle suits candidates with deadlines. Google favours engineers whose strength is conceptual depth and clean fundamentals over service-catalogue knowledge. If your resume reads infrastructure-operator, start with Amazon; if it reads computer-science-generalist, Google plays to your strengths.

Read the standalone interview guides

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