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Data engineering interview · Data & Cloud Platforms

Snowflake

SQL is critical, plus warehousing + Spark optimization and distinctive data-platform system design (quota service, scheduler).

Stages: 64 with reported questions
Difficulty: Not rated
Cloud: Snowflake on AWS/Azure/GCP
Evidence: 1 of 2sources are first-hand

What Snowflake does

And what its data engineers work with.

Business: Cloud data platform / warehouse
Platform: Snowflake on AWS/Azure/GCP; Spark, Python, Java
Stack named in its interviews: SQL
Python
Spark
Data warehousing

The loop

Stages in the order candidates meet them.

01Recruiter
02Hiring manager
03SQL test
04Technical (coding/design)
05Behavioral + case / fit
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
SQL
critical
High confidence
- SQL
2 reported questions. See them below
ROUND 03
DSA
Medium confidence
- Python
1 reported question. See them below
ROUND 04
Big Data / Warehousing
Medium confidence
- Design
- Spark
- Cloud and tools
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

Online AssessmentMedium confidence

SQL + basic Python + light statistics / data-processing logicPractise SQL

SQLHigh confidence

Complex joins, aggregations, data transformationsPractise SQL
Optimize a slow queryPractise SQL

DSAMedium confidence

Stack vs queue, binary search, cycle detection in a linked list, dynamic programming

Big Data / WarehousingMedium confidence

ETL, data pipeline design, cloud data architecturePractise Data Pipeline
Spark optimization; data integration + performance tuningPractise Apache Spark

Expected written from Snowflake's standards

Nobody reported these exact questions at Snowflake. 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.

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

Python and codingReported1 reported question across 2 rounds
SQLReported3 reported questions across 2 rounds
Pipeline and system designReported1 reported question across 1 round
Spark and big dataReported1 reported question across 1 round
Cloud and toolsReported1 reported question across 1 round
Behavioral and communicationIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

SQL (complex joins/aggregations/transformations, optimize slow queries)
Do first (4)
Warehousing + Spark optimization
Data-platform system design (quota/scheduler/pipeline)
DSA (DP, cycle detection)
Python data manipulation
Then
Behavioral + case

Rejection patterns

Why candidates were turned down, as reported.

Weak SQL (critical here)
SQL is named in 37 of 50 company accounts.
Cannot optimize a slow query
SQL is named in 37 of 50 company accounts.
Generic system design instead of data-platform thinking
Design is named in 27 of 50 company accounts.

Reported experiences

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

5 accounts

Data EngineerDate not statedReportedCandidate account
A technical round covering SQL, Python, big data ideas, warehousing and Spark tuning. SQL had complex joins and aggregations, and the talk went on to pipeline design, connecting data sources and performance tuning.
What to take from it Be ready to move from SQL to design in the same round.
Senior Data EngineerBellevue, USGlassdoor
In Bellevue: a design round on matching data across systems, a Python and SQL round at roughly easy LeetCode level, and several leadership rounds with the hiring manager and partner teams. The candidate found the process disorganised.
What to take from it Have a clear way to match records from two systems.
Questions named
- Design data matching across systems
Data EngineerPune, India · 2023Glassdoor
Three rounds of medium difficulty over about a week. This candidate was in Pune in 2023.
What to take from it The pace can be quick, so prepare before the first call.
Data EngineerDate not statedInterview Query
In the online test, the last problem was almost right but too slow. The same account says the take-home tasks look for more than clever algorithms.
What to take from it Check the time cost of your solution, and write clean code.
Composite walkthroughs. Built from the company's documented loop, not from one person's account.
The Online Assessment roundDate not statedComposite
This is a composite, not one person's account. It is built from the loop we have on record for Snowflake. Questions reported for this round: SQL + basic Python + light statistics / data-processing logic.
What to take from it Practise these questions before you reach this round.
Questions named
- SQL + basic Python + light statistics / data-processing logic

Sources and how sure we are

SQL is the gate; system design is data-platform-flavored, not generic web services.

Medium — Nishchay Agarwal, "Data Engineer Interview Experience for Snowflake"
datainterview — Snowflake Data Engineer interview (guide, not a first-hand account)

Rehearse the Snowflake loop

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

Online Assessment · 1 of 6

SQL + basic Python + light statistics / data-processing logic

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

Week 1
Python and coding
Read: Python chapters Drill: Python practice Say it aloud: Python questions Full plan: Ace the Python round
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
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: AWS questions
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
Week 2
Rehearse the Snowflake loop
Run the question deckThen repeat your weakest topic once more.

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All interview experiences

Data engineering interview · Data & Cloud Platforms

Snowflake

SQL is critical, plus warehousing + Spark optimization and distinctive data-platform system design (quota service, scheduler).

Stages: 64 with reported questions
Difficulty: Not rated
Cloud: Snowflake on AWS/Azure/GCP
Evidence: 1 of 2sources are first-hand

What Snowflake does

And what its data engineers work with.

Business: Cloud data platform / warehouse
Platform: Snowflake on AWS/Azure/GCP; Spark, Python, Java
Stack named in its interviews: SQL
Python
Spark
Data warehousing

The loop

Stages in the order candidates meet them.

01Recruiter
02Hiring manager
03SQL test
04Technical (coding/design)
05Behavioral + case / fit
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
SQL
critical
High confidence
- SQL
2 reported questions. See them below
ROUND 03
DSA
Medium confidence
- Python
1 reported question. See them below
ROUND 04
Big Data / Warehousing
Medium confidence
- Design
- Spark
- Cloud and tools
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

Online AssessmentMedium confidence

SQL + basic Python + light statistics / data-processing logicPractise SQL

SQLHigh confidence

Complex joins, aggregations, data transformationsPractise SQL
Optimize a slow queryPractise SQL

DSAMedium confidence

Stack vs queue, binary search, cycle detection in a linked list, dynamic programming

Big Data / WarehousingMedium confidence

ETL, data pipeline design, cloud data architecturePractise Data Pipeline
Spark optimization; data integration + performance tuningPractise Apache Spark

Expected written from Snowflake's standards

Nobody reported these exact questions at Snowflake. 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.

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

Python and codingReported1 reported question across 2 rounds
SQLReported3 reported questions across 2 rounds
Pipeline and system designReported1 reported question across 1 round
Spark and big dataReported1 reported question across 1 round
Cloud and toolsReported1 reported question across 1 round
Behavioral and communicationIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

SQL (complex joins/aggregations/transformations, optimize slow queries)
Do first (4)
Warehousing + Spark optimization
Data-platform system design (quota/scheduler/pipeline)
DSA (DP, cycle detection)
Python data manipulation
Then
Behavioral + case

Rejection patterns

Why candidates were turned down, as reported.

Weak SQL (critical here)
SQL is named in 37 of 50 company accounts.
Cannot optimize a slow query
SQL is named in 37 of 50 company accounts.
Generic system design instead of data-platform thinking
Design is named in 27 of 50 company accounts.

Reported experiences

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

5 accounts

Data EngineerDate not statedReportedCandidate account
A technical round covering SQL, Python, big data ideas, warehousing and Spark tuning. SQL had complex joins and aggregations, and the talk went on to pipeline design, connecting data sources and performance tuning.
What to take from it Be ready to move from SQL to design in the same round.
Senior Data EngineerBellevue, USGlassdoor
In Bellevue: a design round on matching data across systems, a Python and SQL round at roughly easy LeetCode level, and several leadership rounds with the hiring manager and partner teams. The candidate found the process disorganised.
What to take from it Have a clear way to match records from two systems.
Questions named
- Design data matching across systems
Data EngineerPune, India · 2023Glassdoor
Three rounds of medium difficulty over about a week. This candidate was in Pune in 2023.
What to take from it The pace can be quick, so prepare before the first call.
Data EngineerDate not statedInterview Query
In the online test, the last problem was almost right but too slow. The same account says the take-home tasks look for more than clever algorithms.
What to take from it Check the time cost of your solution, and write clean code.
Composite walkthroughs. Built from the company's documented loop, not from one person's account.
The Online Assessment roundDate not statedComposite
This is a composite, not one person's account. It is built from the loop we have on record for Snowflake. Questions reported for this round: SQL + basic Python + light statistics / data-processing logic.
What to take from it Practise these questions before you reach this round.
Questions named
- SQL + basic Python + light statistics / data-processing logic

Sources and how sure we are

SQL is the gate; system design is data-platform-flavored, not generic web services.

Medium — Nishchay Agarwal, "Data Engineer Interview Experience for Snowflake"
datainterview — Snowflake Data Engineer interview (guide, not a first-hand account)

Rehearse the Snowflake loop

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

Online Assessment · 1 of 6

SQL + basic Python + light statistics / data-processing logic

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

Week 1
Python and coding
Read: Python chapters Drill: Python practice Say it aloud: Python questions Full plan: Ace the Python round
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
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: AWS questions
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
Week 2
Rehearse the Snowflake loop
Run the question deckThen repeat your weakest topic once more.

What Snowflake does

The loop

What each round expects

Online Assessment

SQL

DSA

Big Data / Warehousing

Reported interview questions

Reported from accounts and guides, by round

Expected written from Snowflake's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Questions named

Rehearse the Snowflake loop

Your preparation plan

Week 1

Week 2

What Snowflake does

The loop

What each round expects

Online Assessment

SQL

DSA

Big Data / Warehousing

Reported interview questions

Reported from accounts and guides, by round

Expected written from Snowflake's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Questions named

Rehearse the Snowflake loop

Your preparation plan

Week 1

Week 2