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Data engineering interview · Big Tech & Enterprise Software

Oracle

Deep-DB heritage: advanced SQL, Python DS/algo (array/sorting), Spark, and warehousing concepts.

Stages: 43 with reported questions
Difficulty: Not rated
Cloud: OCI
Evidence: 1 of 2sources are first-hand

What Oracle does

And what its data engineers work with.

Business: Enterprise database / cloud
Platform: OCI; Oracle DB, Spark, Python
Stack named in its interviews: SQL
Python
Spark
Data warehousing

The loop

Stages in the order candidates meet them.

01Technical phone/video
02Onsite (discussions/whiteboarding)
03Behavioral
04Offer

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

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
SQL
Medium confidence
- SQL
2 reported questions. See them below
ROUND 02
Python
Medium confidence
- Python
2 reported questions. See them below
ROUND 03
Spark / Warehousing
Medium confidence
- Spark
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

SQLMedium confidence

Joins, subqueries, window functions, performance optimizationPractise SQL
Retrieve plant names and their associated weather typesPractise SQL

PythonMedium confidence

Array manipulation + sorting; DS/algo; OOP; pandas/numpyPractise Python
Find pairs of numbers with the minimum absolute differencePractise Python

Spark / WarehousingMedium confidence

Spark for large-scale processingPractise Apache Spark
Data warehousing + pipeline conceptsPractise Data Pipeline

Expected written from Oracle's standards

Nobody reported these exact questions at Oracle. 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.

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
Group events into sessions with a 30-minute gap.Python
Count by key in a file bigger than memory.Python
Tell me about a pipeline you owned that failed in production.Behavioral
Describe a disagreement about a design, and how it ended.Behavioral
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

Must know

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

Spark and big dataReported1 reported question across 1 round
SQLReported1 reported question across 1 round
Python and codingIn the prioritiesNamed in the prep priorities
Behavioral and communicationIn the prioritiesNamed in the prep priorities
Pipeline and system designIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

SQL (joins, subqueries, window, optimization)
Do first (3)
Python DS/algo (arrays, sorting, pairs) + pandas/numpy
Spark; warehousing concepts
Pipeline/warehouse design
Then
Behavioral

Rejection patterns

Why candidates were turned down, as reported.

Weak advanced SQL/optimization
SQL is named in 37 of 50 company accounts.
Shaky Python DS/algo
Python is named in 13 of 50 company accounts.
Thin warehousing/Spark knowledge
Spark is named in 25 of 50 company accounts.

Reported experiences

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

5 accounts

Data EngineerDate not statedReportedCandidate account
The technical rounds covered SQL (joins, subqueries, window functions), Python (arrays and sorting, pairs with the smallest difference), Spark and data warehousing.
What to take from it Cover SQL, Python and Spark. The questions were not unusual.
Data EngineerBengaluru, India · 2025Glassdoor
In Bengaluru in 2025 the process was short: one SQL coding question, about easy to medium, and one chat with a current employee. The questions were about data engineering and databases.
What to take from it Some loops are short. Do not skip the basics.
Data EngineerDate not statedGlassdoor
Five rounds in total. The last round was HR and behavioral. Earlier rounds had SQL, Python and scenario questions.
What to take from it Prepare for the HR round too.
Data EngineerDate not statedGlassdoor
A candidate was asked about Snowflake and SQL. A report from Casablanca had system design and a talk about tools from the candidate's resume.
What to take from it Know every tool on your resume.
Questions named
- Snowflake
- SQL
Data EngineerDate not statedGlassdoor
Results differ. One candidate finished two rounds and never heard back. Another got an offer in two weeks.
What to take from it Timelines vary a lot, so ask for next steps after each round.

Sources and how sure we are

One solid first-hand account; SQL + Python DS/algo + warehousing are the core.

Medium — Gunjan Sahu, "Oracle Data Engineer Interview Experience"
InterviewQuery — Oracle Data Engineer guide (guide, not a first-hand account)

Rehearse the Oracle loop

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

SQL · 1 of 6

Joins, subqueries, window functions, performance optimization

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

Week 1
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
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
Pipeline and system design
Read: Data pipeline chapters Drill: Pipeline practice Say it aloud: Data pipeline questions Full plan: Ace the pipeline round
Week 2
Rehearse the Oracle 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

Oracle

Deep-DB heritage: advanced SQL, Python DS/algo (array/sorting), Spark, and warehousing concepts.

Stages: 43 with reported questions
Difficulty: Not rated
Cloud: OCI
Evidence: 1 of 2sources are first-hand

What Oracle does

And what its data engineers work with.

Business: Enterprise database / cloud
Platform: OCI; Oracle DB, Spark, Python
Stack named in its interviews: SQL
Python
Spark
Data warehousing

The loop

Stages in the order candidates meet them.

01Technical phone/video
02Onsite (discussions/whiteboarding)
03Behavioral
04Offer

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

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
SQL
Medium confidence
- SQL
2 reported questions. See them below
ROUND 02
Python
Medium confidence
- Python
2 reported questions. See them below
ROUND 03
Spark / Warehousing
Medium confidence
- Spark
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

SQLMedium confidence

Joins, subqueries, window functions, performance optimizationPractise SQL
Retrieve plant names and their associated weather typesPractise SQL

PythonMedium confidence

Array manipulation + sorting; DS/algo; OOP; pandas/numpyPractise Python
Find pairs of numbers with the minimum absolute differencePractise Python

Spark / WarehousingMedium confidence

Spark for large-scale processingPractise Apache Spark
Data warehousing + pipeline conceptsPractise Data Pipeline

Expected written from Oracle's standards

Nobody reported these exact questions at Oracle. 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.

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
Group events into sessions with a 30-minute gap.Python
Count by key in a file bigger than memory.Python
Tell me about a pipeline you owned that failed in production.Behavioral
Describe a disagreement about a design, and how it ended.Behavioral
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

Must know

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

Spark and big dataReported1 reported question across 1 round
SQLReported1 reported question across 1 round
Python and codingIn the prioritiesNamed in the prep priorities
Behavioral and communicationIn the prioritiesNamed in the prep priorities
Pipeline and system designIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

SQL (joins, subqueries, window, optimization)
Do first (3)
Python DS/algo (arrays, sorting, pairs) + pandas/numpy
Spark; warehousing concepts
Pipeline/warehouse design
Then
Behavioral

Rejection patterns

Why candidates were turned down, as reported.

Weak advanced SQL/optimization
SQL is named in 37 of 50 company accounts.
Shaky Python DS/algo
Python is named in 13 of 50 company accounts.
Thin warehousing/Spark knowledge
Spark is named in 25 of 50 company accounts.

Reported experiences

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

5 accounts

Data EngineerDate not statedReportedCandidate account
The technical rounds covered SQL (joins, subqueries, window functions), Python (arrays and sorting, pairs with the smallest difference), Spark and data warehousing.
What to take from it Cover SQL, Python and Spark. The questions were not unusual.
Data EngineerBengaluru, India · 2025Glassdoor
In Bengaluru in 2025 the process was short: one SQL coding question, about easy to medium, and one chat with a current employee. The questions were about data engineering and databases.
What to take from it Some loops are short. Do not skip the basics.
Data EngineerDate not statedGlassdoor
Five rounds in total. The last round was HR and behavioral. Earlier rounds had SQL, Python and scenario questions.
What to take from it Prepare for the HR round too.
Data EngineerDate not statedGlassdoor
A candidate was asked about Snowflake and SQL. A report from Casablanca had system design and a talk about tools from the candidate's resume.
What to take from it Know every tool on your resume.
Questions named
- Snowflake
- SQL
Data EngineerDate not statedGlassdoor
Results differ. One candidate finished two rounds and never heard back. Another got an offer in two weeks.
What to take from it Timelines vary a lot, so ask for next steps after each round.

Sources and how sure we are

One solid first-hand account; SQL + Python DS/algo + warehousing are the core.

Medium — Gunjan Sahu, "Oracle Data Engineer Interview Experience"
InterviewQuery — Oracle Data Engineer guide (guide, not a first-hand account)

Rehearse the Oracle loop

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

SQL · 1 of 6

Joins, subqueries, window functions, performance optimization

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

Week 1
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
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
Pipeline and system design
Read: Data pipeline chapters Drill: Pipeline practice Say it aloud: Data pipeline questions Full plan: Ace the pipeline round
Week 2
Rehearse the Oracle loop
Run the question deckThen repeat your weakest topic once more.

What Oracle does

The loop

What each round expects

SQL

Python

Spark / Warehousing

Reported interview questions

Reported from accounts and guides, by round

Expected written from Oracle's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Rehearse the Oracle loop

Your preparation plan

Week 1

Week 2

What Oracle does

The loop

What each round expects

SQL

Python

Spark / Warehousing

Reported interview questions

Reported from accounts and guides, by round

Expected written from Oracle's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Rehearse the Oracle loop

Your preparation plan

Week 1

Week 2