LinkedIn — Data Engineering Interview Experience

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Data engineering interview · Big Tech & Enterprise Software

Phone split 30/30 Python+SQL, then a loop emphasizing layered SQL, data modeling/ETL, and production-ready system design.

Stages: 44 with reported questions
Difficulty: Not rated
Cloud: Azure + large on-prem
Evidence: 1 of 2sources are first-hand

What LinkedIn does

And what its data engineers work with.

Business: Professional network / SaaS (Microsoft)
Platform: Azure + large on-prem; Kafka, Spark, Pinot, Hadoop
Stack named in its interviews: SQL
Python
Spark
Kafka
Snowflake

The loop

Stages in the order candidates meet them.

01Recruiter
02Phone (30 Python + 30 SQL)
03Onsite (4–6)
04Offer

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

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
Phone screen
High confidence
- SQL
- Python
2 reported questions. See them below
ROUND 02
SQL
High confidence
- SQL
2 reported questions. See them below
ROUND 03
Data Modeling / Architecture
High confidence
- Modeling
- Design
- Spark
2 reported questions. See them below
ROUND 04
Statistics
Medium confidence
1 reported question. 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

Phone screenHigh confidence

30 min Python (data manipulation + algorithmic)Practise Python
30 min SQL (joins, aggregations, window functions)Practise SQL

SQLHigh confidence

Complex, layered queries — joins, aggregations, window functionsPractise SQL
Sessionize member activity; top-N; dedupPractise SQL

Data Modeling / ArchitectureHigh confidence

ETL, data warehousing, scalable architecturesPractise Data Pipeline
Star/snowflake, SCD, partitioningPractise Apache Spark

StatisticsMedium confidence

Basic statistics round

Expected written from LinkedIn's standards

Nobody reported these exact questions at LinkedIn. 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 star schema for orders, and state the grain first.Modeling
How do you keep history when a customer's address changes?Modeling
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
Group events into sessions with a 30-minute gap.Python
Count by key in a file bigger than memory.Python
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
What does a consumer group do, and what happens in a rebalance?Design
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.

SQLReported2 reported questions across 2 rounds
Data modelingReported1 reported question across 1 round
Pipeline and system designReported1 reported question across 1 round
Python and codingReported1 reported question across 1 round
Spark and big dataReported1 reported question across 1 round
Cloud and toolsIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

Complex layered SQL + window functions
Do first (3)
Data modeling/ETL/warehousing
Production-ready system design (streaming vs batch, lakes vs warehouses, monitoring)
Python data manipulation + algorithms
Then
Basic statistics
Kafka/Spark

Rejection patterns

Why candidates were turned down, as reported.

Flashy architecture without fault tolerance/consistency/maintainability
Design is named in 27 of 50 company accounts.
Weak data modeling
Modeling is named in 14 of 50 company accounts.
Shaky layered SQL
SQL is named in 37 of 50 company accounts.

Reported experiences

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

5 accounts

Data EngineerDate not statedReportedCandidate account
A phone screen with Python and SQL, then an onsite with the hiring manager, SQL data work, data architecture and modeling for ETL, statistics, algorithms and system design.
What to take from it Prepare for a wide onsite: SQL, modeling and design.
Senior Data EngineerDate not statedRejectedprachub
A senior candidate wrote that they did well in the coding round and were rejected the next day. Another 2026 account says the role leaned more towards software engineering than data engineering.
What to take from it Check what the team really builds, and prepare for software-style coding if it is heavy.
Data EngineerDate not statedReports summarised from Glassdoor and prep sites
A one-hour coding round: about 25 minutes on an algorithm (longest substring without repeating characters) and about 25 minutes on SQL with a follow-up.
What to take from it Practise an algorithm and a SQL question in one sitting.
Questions named
- Longest substring without repeating characters
- A SQL question with a follow-up
Data EngineerDate not statedReports summarised from Glassdoor and prep sites
SQL questions have covered mutual connections, degrees of separation, influence measures and window functions.
What to take from it These are graph-style questions in SQL. Practise self-joins.
Questions named
- Mutual connections
- Degrees of separation
- Influence metrics
Data EngineerDate not statedReports summarised from Glassdoor and prep sites
Modeling questions covered slowly changing dimensions, star schemas, facts and dimensions. One round asked the candidate to review the design of their hardest project. The hiring manager round asked about projects, skills and why LinkedIn.
What to take from it Be ready to explain your hardest project's design and why you chose it.
Questions named
- Slowly changing dimensions
- Star schema, facts and dimensions
- Your most challenging project architecture

Sources and how sure we are

Good first-hand signal. Production-ready design (fault tolerance, consistency) matters more than novelty.

Medium — Archana Goyal, "LinkedIn Data Engineer Interview Experience"
datainterview — LinkedIn Data Engineer interview (guide, not a first-hand account)

Rehearse the LinkedIn loop

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

Phone screen · 1 of 7

30 min Python (data manipulation + algorithmic)

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

Practise Python

00:00

Your preparation plan

Pick your level and the weeks you have.

Built from what LinkedIn 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
Data modeling
Read: Data modeling chapters Drill: Modeling practice Say it aloud: Data modeling questions Full plan: Ace the modeling round
Pipeline and system design
Read: Data pipeline chapters Drill: Pipeline practice Say it aloud: Data pipeline questions Full plan: Ace the pipeline round
Python and coding
Read: Python chapters Drill: Python practice Say it aloud: Python questions Full plan: Ace the Python round
Spark and big data
Read: Spark chapters Drill: Spark playground Say it aloud: Spark questions Full plan: Ace the Spark round
Cloud and tools
Read: Data pipeline chapters Say it aloud: Azure questions
Week 2
Rehearse the LinkedIn 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

Phone split 30/30 Python+SQL, then a loop emphasizing layered SQL, data modeling/ETL, and production-ready system design.

Stages: 44 with reported questions
Difficulty: Not rated
Cloud: Azure + large on-prem
Evidence: 1 of 2sources are first-hand

What LinkedIn does

And what its data engineers work with.

Business: Professional network / SaaS (Microsoft)
Platform: Azure + large on-prem; Kafka, Spark, Pinot, Hadoop
Stack named in its interviews: SQL
Python
Spark
Kafka
Snowflake

The loop

Stages in the order candidates meet them.

01Recruiter
02Phone (30 Python + 30 SQL)
03Onsite (4–6)
04Offer

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

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
Phone screen
High confidence
- SQL
- Python
2 reported questions. See them below
ROUND 02
SQL
High confidence
- SQL
2 reported questions. See them below
ROUND 03
Data Modeling / Architecture
High confidence
- Modeling
- Design
- Spark
2 reported questions. See them below
ROUND 04
Statistics
Medium confidence
1 reported question. 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

Phone screenHigh confidence

30 min Python (data manipulation + algorithmic)Practise Python
30 min SQL (joins, aggregations, window functions)Practise SQL

SQLHigh confidence

Complex, layered queries — joins, aggregations, window functionsPractise SQL
Sessionize member activity; top-N; dedupPractise SQL

Data Modeling / ArchitectureHigh confidence

ETL, data warehousing, scalable architecturesPractise Data Pipeline
Star/snowflake, SCD, partitioningPractise Apache Spark

StatisticsMedium confidence

Basic statistics round

Expected written from LinkedIn's standards

Nobody reported these exact questions at LinkedIn. 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 star schema for orders, and state the grain first.Modeling
How do you keep history when a customer's address changes?Modeling
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
Group events into sessions with a 30-minute gap.Python
Count by key in a file bigger than memory.Python
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
What does a consumer group do, and what happens in a rebalance?Design
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.

SQLReported2 reported questions across 2 rounds
Data modelingReported1 reported question across 1 round
Pipeline and system designReported1 reported question across 1 round
Python and codingReported1 reported question across 1 round
Spark and big dataReported1 reported question across 1 round
Cloud and toolsIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

Complex layered SQL + window functions
Do first (3)
Data modeling/ETL/warehousing
Production-ready system design (streaming vs batch, lakes vs warehouses, monitoring)
Python data manipulation + algorithms
Then
Basic statistics
Kafka/Spark

Rejection patterns

Why candidates were turned down, as reported.

Flashy architecture without fault tolerance/consistency/maintainability
Design is named in 27 of 50 company accounts.
Weak data modeling
Modeling is named in 14 of 50 company accounts.
Shaky layered SQL
SQL is named in 37 of 50 company accounts.

Reported experiences

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

5 accounts

Data EngineerDate not statedReportedCandidate account
A phone screen with Python and SQL, then an onsite with the hiring manager, SQL data work, data architecture and modeling for ETL, statistics, algorithms and system design.
What to take from it Prepare for a wide onsite: SQL, modeling and design.
Senior Data EngineerDate not statedRejectedprachub
A senior candidate wrote that they did well in the coding round and were rejected the next day. Another 2026 account says the role leaned more towards software engineering than data engineering.
What to take from it Check what the team really builds, and prepare for software-style coding if it is heavy.
Data EngineerDate not statedReports summarised from Glassdoor and prep sites
A one-hour coding round: about 25 minutes on an algorithm (longest substring without repeating characters) and about 25 minutes on SQL with a follow-up.
What to take from it Practise an algorithm and a SQL question in one sitting.
Questions named
- Longest substring without repeating characters
- A SQL question with a follow-up
Data EngineerDate not statedReports summarised from Glassdoor and prep sites
SQL questions have covered mutual connections, degrees of separation, influence measures and window functions.
What to take from it These are graph-style questions in SQL. Practise self-joins.
Questions named
- Mutual connections
- Degrees of separation
- Influence metrics
Data EngineerDate not statedReports summarised from Glassdoor and prep sites
Modeling questions covered slowly changing dimensions, star schemas, facts and dimensions. One round asked the candidate to review the design of their hardest project. The hiring manager round asked about projects, skills and why LinkedIn.
What to take from it Be ready to explain your hardest project's design and why you chose it.
Questions named
- Slowly changing dimensions
- Star schema, facts and dimensions
- Your most challenging project architecture

Sources and how sure we are

Good first-hand signal. Production-ready design (fault tolerance, consistency) matters more than novelty.

Medium — Archana Goyal, "LinkedIn Data Engineer Interview Experience"
datainterview — LinkedIn Data Engineer interview (guide, not a first-hand account)

Rehearse the LinkedIn loop

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

Phone screen · 1 of 7

30 min Python (data manipulation + algorithmic)

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

Practise Python

00:00

Your preparation plan

Pick your level and the weeks you have.

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

What LinkedIn does

The loop

What each round expects

Phone screen

SQL

Data Modeling / Architecture

Statistics

Reported interview questions

Reported from accounts and guides, by round

Expected written from LinkedIn's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Questions named

Questions named

Rehearse the LinkedIn loop

Your preparation plan

Week 1

Week 2

What LinkedIn does

The loop

What each round expects

Phone screen

SQL

Data Modeling / Architecture

Statistics

Reported interview questions

Reported from accounts and guides, by round

Expected written from LinkedIn's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Questions named

Questions named

Rehearse the LinkedIn loop

Your preparation plan

Week 1

Week 2