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Data engineering interview · Big Tech & Enterprise Software

Apple

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

Stages: 54 with reported questions
Difficulty: Not rated
Cloud: Mixed on-prem + cloud
Evidence: 2 of 3sources are first-hand

What Apple does

And what its data engineers work with.

Business: Consumer hardware / software / services
Platform: Mixed on-prem + cloud; JD tech: Python, SQL, Spark, Kafka, Snowflake, Airflow
Stack named in its interviews: SQL
Python
Spark
Kafka
Snowflake

The loop

Stages in the order candidates meet them.

01HM phone screen
02Technical phone
03Full-day onsite (4)
04Senior-member round
05Offer

5 stages. 4 rounds have reported questions, described below.

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
HM phone screen
Medium confidence
- SQL
- Python
2 reported questions. See them below
ROUND 02
Technical phone
Medium confidence
- SQL
- Spark
- Python
- Cloud and tools
2 reported questions. See them below
ROUND 03
SQL (onsite)
High confidence
- SQL
- Modeling
- Design
3 reported questions. See them below
ROUND 04
DB design / Data modeling
High confidence
- Modeling
- Design
2 reported questions. See them below

Reported interview questions

By round. Questions marked Expected are written from the company's standards.

Reported from accounts and guides, by round

HM phone screenMedium confidence

Medium SQL + PythonPractise SQL
Early depth + fit check

Technical phoneMedium confidence

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

SQL (onsite)High confidence

Multi-step joins, window functions, CTEs, pivotsPractise SQL
Market basket analysisPractise SQL
Schema-design queriesPractise Data Modeling

DB design / Data modelingHigh confidence

Schema/DB design (dedicated round)Practise Data Modeling
Star/snowflake, SCD, normalize vs denormalizePractise Data Modeling

Expected written from Apple's standards

Nobody reported these exact questions at Apple. They are the questions a loop for each of its top topics is known to ask, so prepare for them as likely, not as confirmed.

Return the top 3 rows per group, and say what happens on ties.SQL
Find the latest row per key in a table that has duplicates.SQL
Design a daily pipeline from an app database to a warehouse that is safe to rerun.Design
How would you handle late and duplicate events?Design
Design a star schema for orders, and state the grain first.Modeling
How do you keep history when a customer's address changes?Modeling
What is a shuffle, and how would you make a job shuffle less?Spark
One hot key makes a join slow. What do you do?Spark
Group events into sessions with a 30-minute gap.Python
Count by key in a file bigger than memory.Python
What does a consumer group do, and what happens in a rebalance?Design
What is the logical date, and why does a daily run start after midnight?Cloud and tools
Why does a warehouse read only the columns you name, and what does that mean for cost?Cloud and tools

Must know

Ranked by how much of the evidence points at each topic.

SQLReported3 reported questions across 3 rounds
Pipeline and system designReported2 reported questions across 2 rounds
Data modelingReported3 reported questions across 2 rounds
Spark and big dataReported1 reported question across 1 round
Python and codingReported2 reported questions across 2 rounds
Cloud and toolsReported1 reported question across 1 round

What matters most

Do the first group before the second.

SQL mastery (joins, window, pivots, schema-design queries, market basket analysis)
Do first (3)
Storage/engine concepts (Parquet vs CSV, Redshift vs Postgres, Spark vs MapReduce)
DB/schema design
Spark internals
Then
End-to-end pipeline design (ICT4+)
Python ETL

Rejection patterns

Why candidates were turned down, as reported.

Weak SQL (Apple DE is SQL-dominant)
SQL is named in 37 of 50 company accounts.
Shaky storage/engine trade-offs
Design is named in 27 of 50 company accounts.
Slow onsite schema design
Modeling is named in 14 of 50 company accounts.

Reported experiences

5 reported. Each opens and closes. Composites are labelled.

5 accounts

Data EngineerDate not statedReportedCandidate account
A hiring manager call, a technical phone screen (Python, ETL, SQL and quick questions on engine trade-offs), then a full-day onsite with coding, database design and data modeling. The SQL was heavy on schema design and included a market basket analysis.
What to take from it Practise designing schemas, not only writing queries.
Data Engineer (Cupertino)Date not statedDeclined offer (positive)Jointaro
A May 2025 report from Cupertino confirms a multi-round onsite. The candidate got an offer and turned it down, and described the experience as positive.
What to take from it A long loop can still end well.
Data EngineerLondon, UKGlassdoor
The first-round task was to build MapReduce in Python, or another language, on HackerRank.
What to take from it Know how map and reduce work well enough to code them.
Questions named
- Implement MapReduce in Python
Data EngineerDate not statedGlassdoor
Scenario questions asked about Parquet and how to join large datasets.
What to take from it Be ready to explain file formats and join choices for big data.
Questions named
- Parquet in a pipeline
- How would you join large datasets?
Data EngineerDate not statedInterview Query
Reported prompts include removing duplicates from a stream, designing a warehouse, and forecasting that keeps updating. One round goes through one or two past projects in detail and asks why you made each choice.
What to take from it Know your own project well enough to defend every choice.
Questions named
- Streaming deduplication
- Warehouse design

Sources and how sure we are

Good first-hand signal. Apple DE is SQL-dominant; teams are siloed so confirm the exact loop with your recruiter.

Medium — Archana Goyal, "Apple Data Engineer Interview Experience"
JoinTaro — Apple Data Engineer, Cupertino (May 2025)
InterviewQuery — Apple Data Engineer guide (guide, not a first-hand account)

Rehearse the Apple loop

Their reported questions, one at a time, against a stopwatch.

HM phone screen · 1 of 9

Medium SQL + Python

Out loud, in order: clarify, build it, check it, explain it.

Practise SQL

00:00

Your preparation plan

Pick your level and the weeks you have.

Built from what Apple asks, strongest first. For the wider basics at your level, follow the Mid level plan alongside it.

Week 1
SQL
Read: SQL chapters Drill: SQL practice Say it aloud: SQL questions Full plan: Ace the SQL round
Pipeline and system design
Read: Data pipeline chapters Drill: Pipeline practice Say it aloud: Data pipeline questions Full plan: Ace the pipeline round
Data modeling
Read: Data modeling chapters Drill: Modeling practice Say it aloud: Data modeling questions Full plan: Ace the modeling round
Spark and big data
Read: Spark chapters Drill: Spark playground Say it aloud: Spark questions Full plan: Ace the Spark round
Python and coding
Read: Python chapters Drill: Python practice Say it aloud: Python questions Full plan: Ace the Python round
Cloud and tools
Read: Data pipeline chapters Say it aloud: Airflow questions
Week 2
Rehearse the Apple loop
Run the question deckThen repeat your weakest topic once more.

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All interview experiences

Data engineering interview · Big Tech & Enterprise Software

Apple

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

Stages: 54 with reported questions
Difficulty: Not rated
Cloud: Mixed on-prem + cloud
Evidence: 2 of 3sources are first-hand

What Apple does

And what its data engineers work with.

Business: Consumer hardware / software / services
Platform: Mixed on-prem + cloud; JD tech: Python, SQL, Spark, Kafka, Snowflake, Airflow
Stack named in its interviews: SQL
Python
Spark
Kafka
Snowflake

The loop

Stages in the order candidates meet them.

01HM phone screen
02Technical phone
03Full-day onsite (4)
04Senior-member round
05Offer

5 stages. 4 rounds have reported questions, described below.

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
HM phone screen
Medium confidence
- SQL
- Python
2 reported questions. See them below
ROUND 02
Technical phone
Medium confidence
- SQL
- Spark
- Python
- Cloud and tools
2 reported questions. See them below
ROUND 03
SQL (onsite)
High confidence
- SQL
- Modeling
- Design
3 reported questions. See them below
ROUND 04
DB design / Data modeling
High confidence
- Modeling
- Design
2 reported questions. See them below

Reported interview questions

By round. Questions marked Expected are written from the company's standards.

Reported from accounts and guides, by round

HM phone screenMedium confidence

Medium SQL + PythonPractise SQL
Early depth + fit check

Technical phoneMedium confidence

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

SQL (onsite)High confidence

Multi-step joins, window functions, CTEs, pivotsPractise SQL
Market basket analysisPractise SQL
Schema-design queriesPractise Data Modeling

DB design / Data modelingHigh confidence

Schema/DB design (dedicated round)Practise Data Modeling
Star/snowflake, SCD, normalize vs denormalizePractise Data Modeling

Expected written from Apple's standards

Nobody reported these exact questions at Apple. They are the questions a loop for each of its top topics is known to ask, so prepare for them as likely, not as confirmed.

Return the top 3 rows per group, and say what happens on ties.SQL
Find the latest row per key in a table that has duplicates.SQL
Design a daily pipeline from an app database to a warehouse that is safe to rerun.Design
How would you handle late and duplicate events?Design
Design a star schema for orders, and state the grain first.Modeling
How do you keep history when a customer's address changes?Modeling
What is a shuffle, and how would you make a job shuffle less?Spark
One hot key makes a join slow. What do you do?Spark
Group events into sessions with a 30-minute gap.Python
Count by key in a file bigger than memory.Python
What does a consumer group do, and what happens in a rebalance?Design
What is the logical date, and why does a daily run start after midnight?Cloud and tools
Why does a warehouse read only the columns you name, and what does that mean for cost?Cloud and tools

Must know

Ranked by how much of the evidence points at each topic.

SQLReported3 reported questions across 3 rounds
Pipeline and system designReported2 reported questions across 2 rounds
Data modelingReported3 reported questions across 2 rounds
Spark and big dataReported1 reported question across 1 round
Python and codingReported2 reported questions across 2 rounds
Cloud and toolsReported1 reported question across 1 round

What matters most

Do the first group before the second.

SQL mastery (joins, window, pivots, schema-design queries, market basket analysis)
Do first (3)
Storage/engine concepts (Parquet vs CSV, Redshift vs Postgres, Spark vs MapReduce)
DB/schema design
Spark internals
Then
End-to-end pipeline design (ICT4+)
Python ETL

Rejection patterns

Why candidates were turned down, as reported.

Weak SQL (Apple DE is SQL-dominant)
SQL is named in 37 of 50 company accounts.
Shaky storage/engine trade-offs
Design is named in 27 of 50 company accounts.
Slow onsite schema design
Modeling is named in 14 of 50 company accounts.

Reported experiences

5 reported. Each opens and closes. Composites are labelled.

5 accounts

Data EngineerDate not statedReportedCandidate account
A hiring manager call, a technical phone screen (Python, ETL, SQL and quick questions on engine trade-offs), then a full-day onsite with coding, database design and data modeling. The SQL was heavy on schema design and included a market basket analysis.
What to take from it Practise designing schemas, not only writing queries.
Data Engineer (Cupertino)Date not statedDeclined offer (positive)Jointaro
A May 2025 report from Cupertino confirms a multi-round onsite. The candidate got an offer and turned it down, and described the experience as positive.
What to take from it A long loop can still end well.
Data EngineerLondon, UKGlassdoor
The first-round task was to build MapReduce in Python, or another language, on HackerRank.
What to take from it Know how map and reduce work well enough to code them.
Questions named
- Implement MapReduce in Python
Data EngineerDate not statedGlassdoor
Scenario questions asked about Parquet and how to join large datasets.
What to take from it Be ready to explain file formats and join choices for big data.
Questions named
- Parquet in a pipeline
- How would you join large datasets?
Data EngineerDate not statedInterview Query
Reported prompts include removing duplicates from a stream, designing a warehouse, and forecasting that keeps updating. One round goes through one or two past projects in detail and asks why you made each choice.
What to take from it Know your own project well enough to defend every choice.
Questions named
- Streaming deduplication
- Warehouse design

Sources and how sure we are

Good first-hand signal. Apple DE is SQL-dominant; teams are siloed so confirm the exact loop with your recruiter.

Medium — Archana Goyal, "Apple Data Engineer Interview Experience"
JoinTaro — Apple Data Engineer, Cupertino (May 2025)
InterviewQuery — Apple Data Engineer guide (guide, not a first-hand account)

Rehearse the Apple loop

Their reported questions, one at a time, against a stopwatch.

HM phone screen · 1 of 9

Medium SQL + Python

Out loud, in order: clarify, build it, check it, explain it.

Practise SQL

00:00

Your preparation plan

Pick your level and the weeks you have.

Built from what Apple asks, strongest first. For the wider basics at your level, follow the Mid level plan alongside it.

Week 1
SQL
Read: SQL chapters Drill: SQL practice Say it aloud: SQL questions Full plan: Ace the SQL round
Pipeline and system design
Read: Data pipeline chapters Drill: Pipeline practice Say it aloud: Data pipeline questions Full plan: Ace the pipeline round
Data modeling
Read: Data modeling chapters Drill: Modeling practice Say it aloud: Data modeling questions Full plan: Ace the modeling round
Spark and big data
Read: Spark chapters Drill: Spark playground Say it aloud: Spark questions Full plan: Ace the Spark round
Python and coding
Read: Python chapters Drill: Python practice Say it aloud: Python questions Full plan: Ace the Python round
Cloud and tools
Read: Data pipeline chapters Say it aloud: Airflow questions
Week 2
Rehearse the Apple loop
Run the question deckThen repeat your weakest topic once more.

What Apple does

The loop

What each round expects

HM phone screen

Technical phone

SQL (onsite)

DB design / Data modeling

Reported interview questions

Reported from accounts and guides, by round

Expected written from Apple's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Questions named

Questions named

Rehearse the Apple loop

Your preparation plan

Week 1

Week 2

What Apple does

The loop

What each round expects

HM phone screen

Technical phone

SQL (onsite)

DB design / Data modeling

Reported interview questions

Reported from accounts and guides, by round

Expected written from Apple's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Questions named

Questions named

Rehearse the Apple loop

Your preparation plan

Week 1

Week 2