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Data engineering interview · Data & Cloud Platforms

MongoDB

Data Pipelines Engineering: DSA coding, strong database fundamentals (NoSQL vs SQL, ACID), and ETL system design.

Stages: 53 with reported questions
Difficulty: Not rated
Cloud: AWS/GCP/Azure (Atlas multi-cloud)
Evidence: 1 of 2sources are first-hand

What MongoDB does

And what its data engineers work with.

Business: Document database / cloud (Atlas)
Platform: AWS/GCP/Azure (Atlas multi-cloud); Spark, Python, Kafka
Stack named in its interviews: Python
Spark
SQL + NoSQL
ETL

The loop

Stages in the order candidates meet them.

01Recruiter (30m)
02Technical phone (2–3 coding)
03Onsite (coding, design, DB, behavioral)
04Hiring manager
05Offer

5 stages. 3 rounds have reported questions, described below.

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
Coding / DSA
Medium confidence
- Python
2 reported questions. See them below
ROUND 02
Technical knowledge
distinctive
Medium confidence
- SQL
4 reported questions. See them below
ROUND 03
System Design
Medium confidence
- Design
- Spark
- Python
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

Coding / DSAMedium confidence

2–3 problems: data structures, algorithmsPractise Python
Some DE-domain questionsPractise Python

Technical knowledgeMedium confidence

NoSQL vs SQLPractise SQL
ACID properties
When to use NoSQL over a SQL DBPractise SQL
Document model, indexing, sharding, aggregation pipelinePractise Data Pipeline

System DesignMedium confidence

Design scalable data pipelines; build ETL with Python + Spark; optimize storagePractise Apache Spark

Expected written from MongoDB's standards

Nobody reported these exact questions at MongoDB. 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
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
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
Tell me about a pipeline you owned that failed in production.Behavioral
Describe a disagreement about a design, and how it ended.Behavioral
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.

SQLReported2 reported questions across 1 round
Python and codingReported2 reported questions across 2 rounds
Pipeline and system designReported1 reported question across 1 round
Spark and big dataReported1 reported question across 1 round
Behavioral and communicationIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

DSA (arrays/hashmaps/strings)
Do first (3)
Database fundamentals (NoSQL vs SQL, ACID, sharding/indexing, document model)
ETL system design (Python + Spark)
SQL analytics
Then
Behavioral

Rejection patterns

Why candidates were turned down, as reported.

Weak database fundamentals (ACID, NoSQL vs SQL)
SQL is named in 37 of 50 company accounts.
Shaky DSA
Python is named in 13 of 50 company accounts.
Shallow ETL/system design
Design is named in 27 of 50 company accounts.

Reported experiences

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

5 accounts

Data EngineerDate not statedReportedCandidate account
Resume shortlist, recruiter call, a coding screen, then an onsite with a design round on an ETL pipeline, questions on NoSQL against SQL and ACID, and behavioral, then the hiring manager.
What to take from it Be ready to compare NoSQL and SQL, and explain ACID.
Campus hire (adjacent role)Sep 2021Naukri Code360
Not a data engineering account. An online multiple-choice round, a coding round, then a video call mixing an algorithm question, a hard SQL query and a deep talk on operating systems.
What to take from it Operating systems knowledge can matter at MongoDB.
Candidate (adjacent role)New Delhi, India · 2022Glassdoor
Not a data engineering account. A 20-question quiz, a coding round on binary search and sorting, a round on core subjects and projects, then HR. This candidate was in New Delhi.
What to take from it Prepare core computer science subjects.
Candidate (adjacent role)2024RejectedGlassdoor
Not a data engineering account. One medium coding question. The candidate was rejected for not knowing enough operating systems and DevOps.
What to take from it Do not skip operating systems and DevOps.
Data Engineer (full-time)New Delhi, India · 2020Glassdoor
Seven rounds: an algorithms screen, two data engineering rounds, a design round, a second algorithms round, a manager round and a behavioral round. The candidate rated the algorithm questions as LeetCode medium.
What to take from it Plan for a long loop with algorithms twice.

Sources and how sure we are

Database fundamentals (ACID, NoSQL vs SQL, when to use which) are the distinctive gate.

Medium — Abhishek Kumar, "My Interview Experience with MongoDB"
InterviewQuery — MongoDB Data Engineer guide (guide, not a first-hand account)

Rehearse the MongoDB loop

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

Coding / DSA · 1 of 7

2–3 problems: data structures, algorithms

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 MongoDB 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
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
Spark and big data
Read: Spark chapters Drill: Spark playground Say it aloud: Spark questions Full plan: Ace the Spark round
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
Week 2
Rehearse the MongoDB loop
Run the question deckThen repeat your weakest topic once more.

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All interview experiences

Data engineering interview · Data & Cloud Platforms

MongoDB

Data Pipelines Engineering: DSA coding, strong database fundamentals (NoSQL vs SQL, ACID), and ETL system design.

Stages: 53 with reported questions
Difficulty: Not rated
Cloud: AWS/GCP/Azure (Atlas multi-cloud)
Evidence: 1 of 2sources are first-hand

What MongoDB does

And what its data engineers work with.

Business: Document database / cloud (Atlas)
Platform: AWS/GCP/Azure (Atlas multi-cloud); Spark, Python, Kafka
Stack named in its interviews: Python
Spark
SQL + NoSQL
ETL

The loop

Stages in the order candidates meet them.

01Recruiter (30m)
02Technical phone (2–3 coding)
03Onsite (coding, design, DB, behavioral)
04Hiring manager
05Offer

5 stages. 3 rounds have reported questions, described below.

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
Coding / DSA
Medium confidence
- Python
2 reported questions. See them below
ROUND 02
Technical knowledge
distinctive
Medium confidence
- SQL
4 reported questions. See them below
ROUND 03
System Design
Medium confidence
- Design
- Spark
- Python
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

Coding / DSAMedium confidence

2–3 problems: data structures, algorithmsPractise Python
Some DE-domain questionsPractise Python

Technical knowledgeMedium confidence

NoSQL vs SQLPractise SQL
ACID properties
When to use NoSQL over a SQL DBPractise SQL
Document model, indexing, sharding, aggregation pipelinePractise Data Pipeline

System DesignMedium confidence

Design scalable data pipelines; build ETL with Python + Spark; optimize storagePractise Apache Spark

Expected written from MongoDB's standards

Nobody reported these exact questions at MongoDB. 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
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
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
Tell me about a pipeline you owned that failed in production.Behavioral
Describe a disagreement about a design, and how it ended.Behavioral
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.

SQLReported2 reported questions across 1 round
Python and codingReported2 reported questions across 2 rounds
Pipeline and system designReported1 reported question across 1 round
Spark and big dataReported1 reported question across 1 round
Behavioral and communicationIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

DSA (arrays/hashmaps/strings)
Do first (3)
Database fundamentals (NoSQL vs SQL, ACID, sharding/indexing, document model)
ETL system design (Python + Spark)
SQL analytics
Then
Behavioral

Rejection patterns

Why candidates were turned down, as reported.

Weak database fundamentals (ACID, NoSQL vs SQL)
SQL is named in 37 of 50 company accounts.
Shaky DSA
Python is named in 13 of 50 company accounts.
Shallow ETL/system design
Design is named in 27 of 50 company accounts.

Reported experiences

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

5 accounts

Data EngineerDate not statedReportedCandidate account
Resume shortlist, recruiter call, a coding screen, then an onsite with a design round on an ETL pipeline, questions on NoSQL against SQL and ACID, and behavioral, then the hiring manager.
What to take from it Be ready to compare NoSQL and SQL, and explain ACID.
Campus hire (adjacent role)Sep 2021Naukri Code360
Not a data engineering account. An online multiple-choice round, a coding round, then a video call mixing an algorithm question, a hard SQL query and a deep talk on operating systems.
What to take from it Operating systems knowledge can matter at MongoDB.
Candidate (adjacent role)New Delhi, India · 2022Glassdoor
Not a data engineering account. A 20-question quiz, a coding round on binary search and sorting, a round on core subjects and projects, then HR. This candidate was in New Delhi.
What to take from it Prepare core computer science subjects.
Candidate (adjacent role)2024RejectedGlassdoor
Not a data engineering account. One medium coding question. The candidate was rejected for not knowing enough operating systems and DevOps.
What to take from it Do not skip operating systems and DevOps.
Data Engineer (full-time)New Delhi, India · 2020Glassdoor
Seven rounds: an algorithms screen, two data engineering rounds, a design round, a second algorithms round, a manager round and a behavioral round. The candidate rated the algorithm questions as LeetCode medium.
What to take from it Plan for a long loop with algorithms twice.

Sources and how sure we are

Database fundamentals (ACID, NoSQL vs SQL, when to use which) are the distinctive gate.

Medium — Abhishek Kumar, "My Interview Experience with MongoDB"
InterviewQuery — MongoDB Data Engineer guide (guide, not a first-hand account)

Rehearse the MongoDB loop

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

Coding / DSA · 1 of 7

2–3 problems: data structures, algorithms

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 MongoDB 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
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
Spark and big data
Read: Spark chapters Drill: Spark playground Say it aloud: Spark questions Full plan: Ace the Spark round
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
Week 2
Rehearse the MongoDB loop
Run the question deckThen repeat your weakest topic once more.

What MongoDB does

The loop

What each round expects

Coding / DSA

Technical knowledge

System Design

Reported interview questions

Reported from accounts and guides, by round

Expected written from MongoDB's standards

Must know

What matters most

Rejection patterns

Reported experiences

Rehearse the MongoDB loop

Your preparation plan

Week 1

Week 2

What MongoDB does

The loop

What each round expects

Coding / DSA

Technical knowledge

System Design

Reported interview questions

Reported from accounts and guides, by round

Expected written from MongoDB's standards

Must know

What matters most

Rejection patterns

Reported experiences

Rehearse the MongoDB loop

Your preparation plan

Week 1

Week 2