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Big Tech & Enterprise Software

Google

Internet / cloud / advertising · Data Engineering interview

GCP-native, SQL + data-modeling heavy, with scale-flavored system design (trillion-row optimization, real-time ad-click pipelines).

Cloud / platform

GCP-native: BigQuery, Dataflow, Pub/Sub, Dataproc, GCS

Reported difficulty

Not reported

SQLPythonBigQueryDataflowAirflowdbt

Hiring process

Recruiter

HM screen

Tech screen(s)

Onsite loop (4–5)

Behavioral (Googleyness)

Offer

Round-by-round breakdown

SQL

· medium–hardHigh confidence

Joins, aggregations, window functions, LAG/LEAD
Query optimization on real data
Performance at scale

Coding

Medium confidence

DS/algo in a language of choice (Python common)
Whiteboard coding

Data Modeling

High confidence

Model event logs for YouTube
Define metrics for Ads reporting
Star schemas, SCDs, table optimization

System Design

High confidence

Optimize a query for a trillion-row dataset
Handle data skew in distributed systems
Design a real-time ad-click analytics pipeline (batch vs streaming, idempotency, late data)

Behavioral (Googleyness)

Medium confidence

Handling pipeline failures
Influencing stakeholders

System & pipeline design prompts

Real-time ad-click analytics pipeline (batch vs streaming, idempotency, late data)
Optimize a query for a trillion-row dataset
Model event logs for YouTube

Prep — high priority

SQL (window/LAG-LEAD, optimization) + modeling (event logs, metrics, star/SCD)
System design: real-time analytics, batch vs streaming, idempotency, skew
BigQuery/Dataflow mental model

Prep — medium priority

Python DS/algo
Googleyness behavioral stories

Rejection patterns

Weak SQL / modeling at scale
Cannot reason about distributed skew or trillion-row optimization
Pipeline design missing idempotency & late-data handling

Reported experiences

Data Engineer

IllustrativeComposite

Composite from aggregator-reported questions: recruiter → HM → tech screen (SQL + coding) → loop (SQL, modeling on YouTube/Ads, system design ad-click pipeline + trillion-row optimization, Googleyness).

Rehearse for Google

Answered questions on what Google 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

Google

Internet / cloud / advertising · Data Engineering interview

GCP-native, SQL + data-modeling heavy, with scale-flavored system design (trillion-row optimization, real-time ad-click pipelines).

Cloud / platform

GCP-native: BigQuery, Dataflow, Pub/Sub, Dataproc, GCS

Reported difficulty

Not reported

SQLPythonBigQueryDataflowAirflowdbt

Hiring process

Recruiter

HM screen

Tech screen(s)

Onsite loop (4–5)

Behavioral (Googleyness)

Offer

Round-by-round breakdown

SQL

· medium–hardHigh confidence

Joins, aggregations, window functions, LAG/LEAD
Query optimization on real data
Performance at scale

Coding

Medium confidence

DS/algo in a language of choice (Python common)
Whiteboard coding

Data Modeling

High confidence

Model event logs for YouTube
Define metrics for Ads reporting
Star schemas, SCDs, table optimization

System Design

High confidence

Optimize a query for a trillion-row dataset
Handle data skew in distributed systems
Design a real-time ad-click analytics pipeline (batch vs streaming, idempotency, late data)

Behavioral (Googleyness)

Medium confidence

Handling pipeline failures
Influencing stakeholders

System & pipeline design prompts

Real-time ad-click analytics pipeline (batch vs streaming, idempotency, late data)
Optimize a query for a trillion-row dataset
Model event logs for YouTube

Prep — high priority

SQL (window/LAG-LEAD, optimization) + modeling (event logs, metrics, star/SCD)
System design: real-time analytics, batch vs streaming, idempotency, skew
BigQuery/Dataflow mental model

Prep — medium priority

Python DS/algo
Googleyness behavioral stories

Rejection patterns

Weak SQL / modeling at scale
Cannot reason about distributed skew or trillion-row optimization
Pipeline design missing idempotency & late-data handling

Reported experiences

Data Engineer

IllustrativeComposite

Rehearse for Google

Answered questions on what Google 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