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Data engineering interview · Data & Cloud Platforms

Databricks

They build Spark — so expect deep Spark/Delta/medallion internals, a hard concurrent-programming round, and trade-off-driven design.

Stages: 64 with reported questions
Difficulty: Not rated
Cloud: Multi-cloud
Evidence: 0 of 2sources are first-hand

What Databricks does

And what its data engineers work with.

Business: Data + AI platform (Lakehouse)
Platform: Multi-cloud; Spark, Delta Lake, Unity Catalog, Auto Loader, Workflows
Stack named in its interviews: SQL
PySpark
Spark
Delta Lake
Unity Catalog

The loop

Stages in the order candidates meet them.

01Recruiter
02Technical phone
03Virtual onsite (4–5h)
04System design
05Behavioral
06Offer

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

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
Technical phone
Medium confidence
- SQL
- Modeling
- Design
- Spark
2 reported questions. See them below
ROUND 02
Coding / Algorithm (×2)
Medium confidence
- Python
1 reported question. See them below
ROUND 03
Concurrent programming
reported "extremely hard"
Medium confidence
1 reported question. See them below
ROUND 04
Spark
central
High confidence
- Design
- Spark
3 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

Technical phoneMedium confidence

SQL queries, Spark optimization, ETL designPractise SQL
Data modeling, schema evolution, performance tuning for large datasetsPractise Data Modeling

Coding / Algorithm (×2)Medium confidence

DS/algo (LeetCode medium); PythonPractise Python

Concurrent programmingMedium confidence

Threads/locks/parallelism

SparkHigh confidence

Spark internals; Delta Lake architecturePractise Apache Spark
Medallion (bronze/silver/gold); Unity Catalog; Auto LoaderPractise Apache Spark
Performance tuning (Z-order, OPTIMIZE/VACUUM)Practise Apache Spark

Expected written from Databricks's standards

Nobody reported these exact questions at Databricks. 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.

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
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
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
Group events into sessions with a 30-minute gap.Python
Count by key in a file bigger than memory.Python

Must know

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

Pipeline and system designReported2 reported questions across 2 rounds
Spark and big dataReported2 reported questions across 2 rounds
SQLReported1 reported question across 1 round
Data modelingReported1 reported question across 1 round
Python and codingReported1 reported question across 1 round
Behavioral and communicationIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

Spark internals + Delta + medallion + Auto Loader + Unity Catalog + tuning
Do first (3)
SQL complex queries
Algo + concurrent programming; system-design trade-offs
Data modeling/warehousing
Then
Orchestration (Workflows/Jobs)
Behavioral impact

Rejection patterns

Why candidates were turned down, as reported.

Cannot articulate system-design trade-offs (batch vs streaming, cost vs perf)
Design is named in 27 of 50 company accounts.
The concurrent-programming round (hard)
Shallow Spark internals at the company that builds Spark
Spark is named in 25 of 50 company accounts.

Reported experiences

5 reported, 1 composite. Each opens and closes. Composites are labelled.

6 accounts

Data EngineerDate not statedGlassdoor
This account was mostly SQL depth: window functions, nested data, moving data around, and handling dates, times and strings.
What to take from it Practise nested data and date handling in SQL.
Questions named
- Window functions
- Nested data
- Date, time and string handling
Data EngineerLondon, UKOffer declinedGlassdoor
A structured SQL test with joins and window functions. The candidate rated it average and turned down the offer.
What to take from it The SQL test is standard, so prepare joins and windows.
Data EngineerSeattle, USGlassdoor
The first two rounds were coding problems much like LeetCode. This candidate was in Seattle.
What to take from it Practise LeetCode-style problems.
Data EngineerDate not statedNo offerGlassdoor
A CodeSignal test that the candidate found very hard. They had no offer and a bad experience.
What to take from it Practise a timed online test before applying.
Data EngineerDate not statedGlassdoor
A panel mixed technical and competency questions. Some candidates also give a final presentation.
What to take from it Prepare a short talk on your work.
Composite walkthroughs. Built from the company's documented loop, not from one person's account.
Data EngineerDate not statedComposite
This is a composite, not one person's account. It follows what candidates report: a recruiter call about Spark and Delta, a phone round with SQL, Spark tuning and modeling, then an onsite with two algorithm rounds, one hard concurrency round, one behavioral round and a design round on a medallion lakehouse.
What to take from it Use it as a map of the loop. Expect coding with real depth.

Sources and how sure we are

No single fetchable transcript; aggregators reflect candidate reports. Deep Spark + concurrency + trade-off design are decisive.

datainterview — Databricks Data Engineer guide (guide, not a first-hand account)
datavidhya — 60 Databricks DE questions (guide, not a first-hand account)

Rehearse the Databricks loop

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

Technical phone · 1 of 7

SQL queries, Spark optimization, ETL design

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 Databricks asks, strongest first. For the wider basics at your level, follow the Mid level plan alongside it.

Week 1
Pipeline and system design
Read: Data pipeline chapters Drill: Pipeline practice Say it aloud: Data pipeline questions Full plan: Ace the pipeline round
Spark and big data
Read: Spark chapters Drill: Spark playground Say it aloud: Spark questions Full plan: Ace the Spark round
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
Python and coding
Read: Python chapters Drill: Python practice Say it aloud: Python questions Full plan: Ace the Python round
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
Week 2
Rehearse the Databricks loop
Run the question deckThen repeat your weakest topic once more.

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All interview experiences

Data engineering interview · Data & Cloud Platforms

Databricks

They build Spark — so expect deep Spark/Delta/medallion internals, a hard concurrent-programming round, and trade-off-driven design.

Stages: 64 with reported questions
Difficulty: Not rated
Cloud: Multi-cloud
Evidence: 0 of 2sources are first-hand

What Databricks does

And what its data engineers work with.

Business: Data + AI platform (Lakehouse)
Platform: Multi-cloud; Spark, Delta Lake, Unity Catalog, Auto Loader, Workflows
Stack named in its interviews: SQL
PySpark
Spark
Delta Lake
Unity Catalog

The loop

Stages in the order candidates meet them.

01Recruiter
02Technical phone
03Virtual onsite (4–5h)
04System design
05Behavioral
06Offer

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

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
Technical phone
Medium confidence
- SQL
- Modeling
- Design
- Spark
2 reported questions. See them below
ROUND 02
Coding / Algorithm (×2)
Medium confidence
- Python
1 reported question. See them below
ROUND 03
Concurrent programming
reported "extremely hard"
Medium confidence
1 reported question. See them below
ROUND 04
Spark
central
High confidence
- Design
- Spark
3 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

Technical phoneMedium confidence

SQL queries, Spark optimization, ETL designPractise SQL
Data modeling, schema evolution, performance tuning for large datasetsPractise Data Modeling

Coding / Algorithm (×2)Medium confidence

DS/algo (LeetCode medium); PythonPractise Python

Concurrent programmingMedium confidence

Threads/locks/parallelism

SparkHigh confidence

Spark internals; Delta Lake architecturePractise Apache Spark
Medallion (bronze/silver/gold); Unity Catalog; Auto LoaderPractise Apache Spark
Performance tuning (Z-order, OPTIMIZE/VACUUM)Practise Apache Spark

Expected written from Databricks's standards

Nobody reported these exact questions at Databricks. 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.

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
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
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
Group events into sessions with a 30-minute gap.Python
Count by key in a file bigger than memory.Python

Must know

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

Pipeline and system designReported2 reported questions across 2 rounds
Spark and big dataReported2 reported questions across 2 rounds
SQLReported1 reported question across 1 round
Data modelingReported1 reported question across 1 round
Python and codingReported1 reported question across 1 round
Behavioral and communicationIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

Spark internals + Delta + medallion + Auto Loader + Unity Catalog + tuning
Do first (3)
SQL complex queries
Algo + concurrent programming; system-design trade-offs
Data modeling/warehousing
Then
Orchestration (Workflows/Jobs)
Behavioral impact

Rejection patterns

Why candidates were turned down, as reported.

Cannot articulate system-design trade-offs (batch vs streaming, cost vs perf)
Design is named in 27 of 50 company accounts.
The concurrent-programming round (hard)
Shallow Spark internals at the company that builds Spark
Spark is named in 25 of 50 company accounts.

Reported experiences

5 reported, 1 composite. Each opens and closes. Composites are labelled.

6 accounts

Data EngineerDate not statedGlassdoor
This account was mostly SQL depth: window functions, nested data, moving data around, and handling dates, times and strings.
What to take from it Practise nested data and date handling in SQL.
Questions named
- Window functions
- Nested data
- Date, time and string handling
Data EngineerLondon, UKOffer declinedGlassdoor
A structured SQL test with joins and window functions. The candidate rated it average and turned down the offer.
What to take from it The SQL test is standard, so prepare joins and windows.
Data EngineerSeattle, USGlassdoor
The first two rounds were coding problems much like LeetCode. This candidate was in Seattle.
What to take from it Practise LeetCode-style problems.
Data EngineerDate not statedNo offerGlassdoor
A CodeSignal test that the candidate found very hard. They had no offer and a bad experience.
What to take from it Practise a timed online test before applying.
Data EngineerDate not statedGlassdoor
A panel mixed technical and competency questions. Some candidates also give a final presentation.
What to take from it Prepare a short talk on your work.
Composite walkthroughs. Built from the company's documented loop, not from one person's account.
Data EngineerDate not statedComposite
This is a composite, not one person's account. It follows what candidates report: a recruiter call about Spark and Delta, a phone round with SQL, Spark tuning and modeling, then an onsite with two algorithm rounds, one hard concurrency round, one behavioral round and a design round on a medallion lakehouse.
What to take from it Use it as a map of the loop. Expect coding with real depth.

Sources and how sure we are

No single fetchable transcript; aggregators reflect candidate reports. Deep Spark + concurrency + trade-off design are decisive.

datainterview — Databricks Data Engineer guide (guide, not a first-hand account)
datavidhya — 60 Databricks DE questions (guide, not a first-hand account)

Rehearse the Databricks loop

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

Technical phone · 1 of 7

SQL queries, Spark optimization, ETL design

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 Databricks asks, strongest first. For the wider basics at your level, follow the Mid level plan alongside it.

Week 1
Pipeline and system design
Read: Data pipeline chapters Drill: Pipeline practice Say it aloud: Data pipeline questions Full plan: Ace the pipeline round
Spark and big data
Read: Spark chapters Drill: Spark playground Say it aloud: Spark questions Full plan: Ace the Spark round
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
Python and coding
Read: Python chapters Drill: Python practice Say it aloud: Python questions Full plan: Ace the Python round
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
Week 2
Rehearse the Databricks loop
Run the question deckThen repeat your weakest topic once more.

What Databricks does

The loop

What each round expects

Technical phone

Coding / Algorithm (×2)

Concurrent programming

Spark

Reported interview questions

Reported from accounts and guides, by round

Expected written from Databricks's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Rehearse the Databricks loop

Your preparation plan

Week 1

Week 2

What Databricks does

The loop

What each round expects

Technical phone

Coding / Algorithm (×2)

Concurrent programming

Spark

Reported interview questions

Reported from accounts and guides, by round

Expected written from Databricks's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Rehearse the Databricks loop

Your preparation plan

Week 1

Week 2