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Big Tech & Enterprise Software

Apple

Consumer hardware / software / services · Data Engineering interview

SQL-dominant and secretive. Heavy SQL + schema design, storage/engine trade-offs, and a full-day onsite per team.

Cloud / platform

Mixed on-prem + cloud; JD tech: Python, SQL, Spark, Kafka, Snowflake, Airflow

Reported difficulty

Not reported

SQLPythonSparkKafkaSnowflake

Hiring process

HM phone screen

Technical phone

Full-day onsite (4)

Senior-member round

Offer

Round-by-round breakdown

HM phone screen

Medium confidence

Medium SQL + Python
Early depth + fit check

Technical phone

Medium confidence

Python basics, ETL, SQL (joins/window/pivots)
Quick-fire: Redshift vs Postgres, Parquet vs CSV, Spark vs MapReduce

SQL (onsite)

High confidence

Multi-step joins, window functions, CTEs, pivots
Market basket analysis
Schema-design queries

DB design / Data modeling

High confidence

Schema/DB design (dedicated round)
Star/snowflake, SCD, normalize vs denormalize

System & pipeline design prompts

End-to-end pipeline: ingestion → processing → storage → access; batch vs streaming (ICT4+)
Large-scale processing & streaming architectures

Prep — high priority

SQL mastery (joins, window, pivots, schema-design queries, market basket analysis)
Storage/engine concepts (Parquet vs CSV, Redshift vs Postgres, Spark vs MapReduce)
DB/schema design

Prep — medium priority

Spark internals
End-to-end pipeline design (ICT4+)
Python ETL

Rejection patterns

Weak SQL (Apple DE is SQL-dominant)
Shaky storage/engine trade-offs
Slow onsite schema design

Reported experiences

Data Engineer

ReportedFirst-hand

HM screen → technical phone (Python/ETL/SQL + quick-fire engine trade-offs) → full-day onsite (coding, DB design, data modeling, senior-member) with SQL-heavy schema design incl. market basket analysis.

Data Engineer (Cupertino)

Declined offer (positive)First-hand

JoinTaro account (May 2025) confirming the multi-round onsite + offer pipeline.

Rehearse for Apple

Answered questions on what Apple 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

Apple

Consumer hardware / software / services · Data Engineering interview

SQL-dominant and secretive. Heavy SQL + schema design, storage/engine trade-offs, and a full-day onsite per team.

Cloud / platform

Mixed on-prem + cloud; JD tech: Python, SQL, Spark, Kafka, Snowflake, Airflow

Reported difficulty

Not reported

SQLPythonSparkKafkaSnowflake

Hiring process

HM phone screen

Technical phone

Full-day onsite (4)

Senior-member round

Offer

Round-by-round breakdown

HM phone screen

Medium confidence

Medium SQL + Python
Early depth + fit check

Technical phone

Medium confidence

Python basics, ETL, SQL (joins/window/pivots)
Quick-fire: Redshift vs Postgres, Parquet vs CSV, Spark vs MapReduce

SQL (onsite)

High confidence

Multi-step joins, window functions, CTEs, pivots
Market basket analysis
Schema-design queries

DB design / Data modeling

High confidence

Schema/DB design (dedicated round)
Star/snowflake, SCD, normalize vs denormalize

System & pipeline design prompts

End-to-end pipeline: ingestion → processing → storage → access; batch vs streaming (ICT4+)
Large-scale processing & streaming architectures

Prep — high priority

SQL mastery (joins, window, pivots, schema-design queries, market basket analysis)
Storage/engine concepts (Parquet vs CSV, Redshift vs Postgres, Spark vs MapReduce)
DB/schema design

Prep — medium priority

Spark internals
End-to-end pipeline design (ICT4+)
Python ETL

Rejection patterns

Weak SQL (Apple DE is SQL-dominant)
Shaky storage/engine trade-offs
Slow onsite schema design

Reported experiences

Data Engineer

ReportedFirst-hand

Data Engineer (Cupertino)

Declined offer (positive)First-hand

JoinTaro account (May 2025) confirming the multi-round onsite + offer pipeline.

Rehearse for Apple

Answered questions on what Apple 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