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Data engineering interview · Big Tech & Enterprise Software

Adobe

Real SDE-level coding bar plus SQL and an Adobe Experience Platform (AEP) system-design round at senior levels.

Stages: 64 with reported questions
Difficulty: ~3 / 5
Cloud: Heavy Azure + AWS
Evidence: 2 of 5sources are first-hand

What Adobe does

And what its data engineers work with.

Business: Creative & digital-experience software
Platform: Heavy Azure + AWS; large Spark/Hadoop, Kafka
Stack named in its interviews: SQL
Python
Spark
AEP
Data modeling

The loop

Stages in the order candidates meet them.

01Recruiter
02Online assessment
03Technical phone
04Onsite (3–5)
05AEP system design (senior)
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
Online Assessment
Medium confidence
- SQL
- Python
1 reported question. See them below
ROUND 02
Coding
SDE-like bar
Medium confidence
- Python
4 reported questions. See them below
ROUND 03
SQL
Medium confidence
- SQL
2 reported questions. See them below
ROUND 04
Spark
Medium confidence
- SQL
- Design
- Spark
4 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

Online AssessmentMedium confidence

DSA problems + SQL queriesPractise SQL

CodingMedium confidence

Find the next greater element in an arrayPractise Python
Two elements in a BST whose sum equals kPractise Python
Top-K most frequent events from a log streamPractise Python
Detect cycles in a dependency graphPractise Python

SQLMedium confidence

Compute a rolling 7-day retention metricPractise SQL
Advanced joins/subqueries/window functions on billions of recordsPractise SQL

SparkMedium confidence

Minimize data skew in a Spark jobPractise Apache Spark
Optimize a slow join between two large datasetsPractise SQL
Fix a Spark job spilling to diskPractise Apache Spark
Fault-tolerant streaming with at-least-once semanticsPractise Data Pipeline

Expected written from Adobe's standards

Nobody reported these exact questions at Adobe. 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
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
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
When would you choose Airflow, and when would you not?Cloud and tools
How do you load files from object storage into a warehouse?Cloud and tools
What does a consumer group do, and what happens in a rebalance?Design

Must know

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

SQLReported3 reported questions across 3 rounds
Spark and big dataReported2 reported questions across 1 round
Python and codingReported1 reported question across 2 rounds
Pipeline and system designReported1 reported question across 1 round
Cloud and toolsIn the prioritiesNamed in the prep priorities
Behavioral and communicationIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

DS/algo coding (stacks, heaps, graphs, BST)
Do first (3)
SQL window functions + rolling metrics
One ETL design (PDF logs S3+Spark+Airflow) end to end
Spark internals (skew/salting, spill, AQE, broadcast)
Then
AEP/real-time CDP concepts for senior
Behavioral STAR

Rejection patterns

Why candidates were turned down, as reported.

Rote "GFG" answers without internals / not handling follow-ups
Weak Spark internals (skew/spill)
Spark is named in 25 of 50 company accounts.
Shallow system design
Design is named in 27 of 50 company accounts.

Reported experiences

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

6 accounts

Data EngineerEdinburgh, UK · Jan 2026No offerGlassdoor
This candidate was asked to design a full solution for a loosely defined problem. The interviewers wanted to hear the assumptions and how the design would grow. The candidate said the loop was about practical scenarios and clear thinking, not memorised facts. The result was no offer.
What to take from it When the problem is vague, state your assumptions first, then say how it scales.
Questions named
- Design an end-to-end solution for a loosely defined problem: state your assumptions, then say how it scales
Data Engineer2023RejectedGlassdoor
A take-home code test came first, then a technical interview. The candidate was surprised that nothing on Spark came up, not even partitions or join types. They were rejected after the technical round.
What to take from it Do not assume Spark questions will come. Prepare coding and SQL too.
Data EngineerSan Jose, US · Dec 2021Not statedGlassdoor
A manager round, a SQL round and a Python round, then a virtual onsite. The coding round was to group words that are anagrams, and the candidate solved it in about 15 minutes. They felt the interviewer was not well prepared.
What to take from it The coding can be simple. Get the basics right and fast.
Questions named
- Group words that are anagrams of each other
Data Engineer (entry level)Seattle, US · Dec 2022Ended after a dayGlassdoor
A recruiter contacted the candidate about a role that needed consulting and client-facing experience. The process ended after about a day.
What to take from it Ask the recruiter early what the team does and what the role needs.
Data EngineerUndated in the listingNo decisionGlassdoor
Four interview rounds, then no decision for over a year. The candidate thought Adobe prefers people in the same country as the engineering team. That is their opinion, not something Adobe says.
What to take from it Ask about timelines, and keep other applications going.
SWE (off-campus, 1–4 yr)Date not statedVariesGeeksforGeeks
Accounts on GeeksforGeeks, mostly for the software track, mention next greater element, two numbers in a binary search tree that add up to k, and coding rounds where the interviewer gives hints. They say interviewers ask how things work inside and what the alternatives are, not for memorised answers. The same coding level is reported for data engineers.
What to take from it Practise data structures and be ready to say why you chose one.
Questions named
- Next greater element in an array
- Two elements in a BST that sum to k

Sources and how sure we are

Adobe-specific data engineer accounts are few. Five Glassdoor reviews and the GeeksforGeeks accounts are listed; Glassdoor blocks automated reading, so each was summarised from its listing and is worth opening in full. The loop shape and round emphasis come from guides, which the sources list marks as not first-hand. The coding bar is reported as SDE-like.

Glassdoor — Adobe Data Engineer interview reviews (rated about 3 / 5 difficulty, 38% positive)
GeeksforGeeks — Adobe interview experiences
DataDriven — Adobe Data Engineer guide (loop and round weights from 7 job descriptions) (guide, not a first-hand account)
InterviewQuery — Adobe Data Engineer guide (guide, not a first-hand account)
codinginterview.com — Adobe Data Engineer guide (guide, not a first-hand account)

Rehearse the Adobe loop

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

Online Assessment · 1 of 11

DSA problems + SQL queries

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 Adobe 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
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
Pipeline and system design
Read: Data pipeline chapters Drill: Pipeline practice Say it aloud: Data pipeline questions Full plan: Ace the pipeline round
Cloud and tools
Read: Data pipeline chapters Say it aloud: AWS questions
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
Week 2
Rehearse the Adobe 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

Adobe

Real SDE-level coding bar plus SQL and an Adobe Experience Platform (AEP) system-design round at senior levels.

Stages: 64 with reported questions
Difficulty: ~3 / 5
Cloud: Heavy Azure + AWS
Evidence: 2 of 5sources are first-hand

What Adobe does

And what its data engineers work with.

Business: Creative & digital-experience software
Platform: Heavy Azure + AWS; large Spark/Hadoop, Kafka
Stack named in its interviews: SQL
Python
Spark
AEP
Data modeling

The loop

Stages in the order candidates meet them.

01Recruiter
02Online assessment
03Technical phone
04Onsite (3–5)
05AEP system design (senior)
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
Online Assessment
Medium confidence
- SQL
- Python
1 reported question. See them below
ROUND 02
Coding
SDE-like bar
Medium confidence
- Python
4 reported questions. See them below
ROUND 03
SQL
Medium confidence
- SQL
2 reported questions. See them below
ROUND 04
Spark
Medium confidence
- SQL
- Design
- Spark
4 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

Online AssessmentMedium confidence

DSA problems + SQL queriesPractise SQL

CodingMedium confidence

Find the next greater element in an arrayPractise Python
Two elements in a BST whose sum equals kPractise Python
Top-K most frequent events from a log streamPractise Python
Detect cycles in a dependency graphPractise Python

SQLMedium confidence

Compute a rolling 7-day retention metricPractise SQL
Advanced joins/subqueries/window functions on billions of recordsPractise SQL

SparkMedium confidence

Minimize data skew in a Spark jobPractise Apache Spark
Optimize a slow join between two large datasetsPractise SQL
Fix a Spark job spilling to diskPractise Apache Spark
Fault-tolerant streaming with at-least-once semanticsPractise Data Pipeline

Expected written from Adobe's standards

Nobody reported these exact questions at Adobe. 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
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
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
When would you choose Airflow, and when would you not?Cloud and tools
How do you load files from object storage into a warehouse?Cloud and tools
What does a consumer group do, and what happens in a rebalance?Design

Must know

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

SQLReported3 reported questions across 3 rounds
Spark and big dataReported2 reported questions across 1 round
Python and codingReported1 reported question across 2 rounds
Pipeline and system designReported1 reported question across 1 round
Cloud and toolsIn the prioritiesNamed in the prep priorities
Behavioral and communicationIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

DS/algo coding (stacks, heaps, graphs, BST)
Do first (3)
SQL window functions + rolling metrics
One ETL design (PDF logs S3+Spark+Airflow) end to end
Spark internals (skew/salting, spill, AQE, broadcast)
Then
AEP/real-time CDP concepts for senior
Behavioral STAR

Rejection patterns

Why candidates were turned down, as reported.

Rote "GFG" answers without internals / not handling follow-ups
Weak Spark internals (skew/spill)
Spark is named in 25 of 50 company accounts.
Shallow system design
Design is named in 27 of 50 company accounts.

Reported experiences

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

6 accounts

Data EngineerEdinburgh, UK · Jan 2026No offerGlassdoor
This candidate was asked to design a full solution for a loosely defined problem. The interviewers wanted to hear the assumptions and how the design would grow. The candidate said the loop was about practical scenarios and clear thinking, not memorised facts. The result was no offer.
What to take from it When the problem is vague, state your assumptions first, then say how it scales.
Questions named
- Design an end-to-end solution for a loosely defined problem: state your assumptions, then say how it scales
Data Engineer2023RejectedGlassdoor
A take-home code test came first, then a technical interview. The candidate was surprised that nothing on Spark came up, not even partitions or join types. They were rejected after the technical round.
What to take from it Do not assume Spark questions will come. Prepare coding and SQL too.
Data EngineerSan Jose, US · Dec 2021Not statedGlassdoor
A manager round, a SQL round and a Python round, then a virtual onsite. The coding round was to group words that are anagrams, and the candidate solved it in about 15 minutes. They felt the interviewer was not well prepared.
What to take from it The coding can be simple. Get the basics right and fast.
Questions named
- Group words that are anagrams of each other
Data Engineer (entry level)Seattle, US · Dec 2022Ended after a dayGlassdoor
A recruiter contacted the candidate about a role that needed consulting and client-facing experience. The process ended after about a day.
What to take from it Ask the recruiter early what the team does and what the role needs.
Data EngineerUndated in the listingNo decisionGlassdoor
Four interview rounds, then no decision for over a year. The candidate thought Adobe prefers people in the same country as the engineering team. That is their opinion, not something Adobe says.
What to take from it Ask about timelines, and keep other applications going.
SWE (off-campus, 1–4 yr)Date not statedVariesGeeksforGeeks
Accounts on GeeksforGeeks, mostly for the software track, mention next greater element, two numbers in a binary search tree that add up to k, and coding rounds where the interviewer gives hints. They say interviewers ask how things work inside and what the alternatives are, not for memorised answers. The same coding level is reported for data engineers.
What to take from it Practise data structures and be ready to say why you chose one.
Questions named
- Next greater element in an array
- Two elements in a BST that sum to k

Sources and how sure we are

Glassdoor — Adobe Data Engineer interview reviews (rated about 3 / 5 difficulty, 38% positive)
GeeksforGeeks — Adobe interview experiences
DataDriven — Adobe Data Engineer guide (loop and round weights from 7 job descriptions) (guide, not a first-hand account)
InterviewQuery — Adobe Data Engineer guide (guide, not a first-hand account)
codinginterview.com — Adobe Data Engineer guide (guide, not a first-hand account)

Rehearse the Adobe loop

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

Online Assessment · 1 of 11

DSA problems + SQL queries

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 Adobe 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
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
Pipeline and system design
Read: Data pipeline chapters Drill: Pipeline practice Say it aloud: Data pipeline questions Full plan: Ace the pipeline round
Cloud and tools
Read: Data pipeline chapters Say it aloud: AWS questions
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
Week 2
Rehearse the Adobe loop
Run the question deckThen repeat your weakest topic once more.

What Adobe does

The loop

What each round expects

Online Assessment

Coding

SQL

Spark

Reported interview questions

Reported from accounts and guides, by round

Expected written from Adobe's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Questions named

Questions named

Rehearse the Adobe loop

Your preparation plan

Week 1

Week 2

What Adobe does

The loop

What each round expects

Online Assessment

Coding

SQL

Spark

Reported interview questions

Reported from accounts and guides, by round

Expected written from Adobe's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Questions named

Questions named

Rehearse the Adobe loop

Your preparation plan

Week 1

Week 2