Master Your Data Engineering Interview

Get personalized feedback on your data pipeline design, ETL processes, and database architecture capabilities

Last updated: December 9, 2025

Data engineering interviews assess your ability to design, build, and maintain scalable data pipelines and infrastructure that enable analytics and machine learning. Expect questions covering ETL/ELT processes, data warehousing, big data technologies, data modeling, and data quality. Success requires demonstrating both technical proficiency with data tools and frameworks alongside understanding of distributed systems, performance optimization, and business requirements.

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Common Data Engineer Interview Questions

Knowing the question isn't enough. Most candidates fail because they never practiced out loud.

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How to Prepare for Your Data Engineering Interview

Master Data Pipeline and ETL Fundamentals

Study ETL vs ELT patterns and when to use each approach

Learn about batch processing frameworks (Apache Spark, Apache Beam) and when to apply them

Understand stream processing concepts and tools (Kafka, Flink, Kinesis) for real-time data

Practice designing data pipelines with proper error handling, retries, and monitoring

Familiarize yourself with workflow orchestration tools (Airflow, Prefect, Dagster)

Strengthen Database and Data Modeling Skills

Review SQL deeply including window functions, CTEs, query optimization, and execution plans

Study data modeling techniques: star schema, snowflake schema, data vault, denormalization strategies

Understand NoSQL databases (MongoDB, Cassandra, DynamoDB) and their use cases

Learn about data warehousing solutions (Snowflake, Redshift, BigQuery) and their architecture

Practice designing for scalability, partitioning strategies, and indexing

Develop Big Data and Cloud Technologies Expertise

Study Hadoop ecosystem components (HDFS, MapReduce, Hive, HBase) and modern alternatives

Learn Apache Spark architecture, RDDs, DataFrames, and optimization techniques

Understand cloud data services across AWS, Azure, and GCP for storage, compute, and analytics

Practice data lake architecture, medallion architecture (bronze/silver/gold layers)

Learn about data governance, cataloging, and metadata management

Key Data Engineer Interview Topics

Common topics and questions you might encounter in your Data Engineer interview

Data Pipeline Design

ETL Architecture
Data Warehousing
Stream Processing
Batch Processing
Data Lake Design

Database Systems

SQL Optimization
NoSQL Solutions
Database Scaling
Data Modeling
Query Performance

Big Data Technologies

Hadoop Ecosystem
Spark
Kafka
Cloud Data Tools
Distributed Systems

Revarta helps you ace your interviews

Built with extensive experience - conducting interviews and passing interviews at Google, NVIDIA, Amazon, Adobe and Remitly

Why Practice Data Engineer Interviews with Revarta

Benefit:Real-World Data Problems

Practice with actual data engineering challenges and pipeline problems faced in tech interviews

Benefit:Identify your gaps

Personalized questions based on your data infrastructure expertise and engineering skills let you immediately discover areas you need to improve on

Benefit:Master problem-solving

Strengthen your responses by practicing areas you're weak in

Benefit:Bite sized practice

Only have 5 minutes? Practice a quick pipeline design or database question

How we increase your success rate

Speak Your Answers

Practice interview questions by speaking out loud (not typing). Hit record and start speaking your answers naturally.

Instant Expert Analysis

Your responses are processed in real-time, transcribing and analyzing your performance.

Get Instant Feedback

Receive detailed analysis and improved answer suggestions. See exactly what's holding you back and how to fix it.

Resources for Data Engineer Interviews

Learn proven strategies and techniques to ace your interview

Guide

STAR Method Interview Guide - Complete Framework with 20+ Examples

Master the STAR method for behavioral interviews. Get the framework, 20+ real examples, and a free template to structure winning answers.

Read article

Guide

Tell Me About Your Greatest Accomplishment - Why 70% Undersell Themselves (And How to Get It Right)

Master "What's your greatest accomplishment?" Learn to choose the right story, showcase your impact, and impress interviewers.

Read article

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FAQ

How does Revarta work?

Revarta conducts live audio interviews where you speak your answers out loud, just like in a real interview. We use your resume, desired job, and company profile to generate relevant questions. Your spoken responses are recorded, transcribed, and analyzed to provide personalized feedback and answer improvements. It's practice that mirrors real interviews - speaking out loud, getting instant feedback, and improving with every session.Read more →

How does the app conduct live audio interviews?

Using your phone or laptop, you answer interview questions by speaking out loud - not typing. The app records your audio responses, transcribes them, and generates personalized feedback based on your actual speaking performance. It's designed to simulate real interview pressure while giving you the safety to practice and improve.Read more →

What types of interviews can I practice?

Revarta specializes in behavioral interviews, which make up the majority of screening and final round questions. We support 80+ job roles including product management, software engineering, consulting, finance, and more. We also cover case study interviews and technical discussion questions. Our question library is constantly expanding based on current interview trends.Read more →

How personalized is the interview experience?

Highly personalized. We use your resume, target job, company profile, and required skills to generate relevant questions. You can even choose different interviewer personas (recruiter, hiring manager, technical lead) to practice different interview stages. Your answers are analyzed for personalized feedback and improvement recommendations specific to your situation.Read more →

How can I practice specific questions?

Yes, You can practice specific questions by using the free form practice questions to find questions that are particularly challenging or of interest to you.Read more →

How is the quality compared to human interviewers?

Our AI is trained on thousands of real interviews conducted by experienced hiring managers. While human coaches excel at strategic career advice, Revarta excels at providing consistent, bias-free feedback on your delivery and content. Most users find the quality comparable to professional interview coaches—without the $200/hour price tag.Read more →

Can I use the app on mobile devices or is it desktop only?

Revarta works on both mobile and desktop. Practice from your phone during your commute or from your laptop at home. Most users practice on mobile for convenience.Read more →

Do I need to commit to a subscription?

No. We offer flexible options: monthly plans with unlimited practice, 1-year plans with unlimited practice, or one-time payment for unlimited lifetime access. You're never locked into long-term commitments.Read more →

Do you offer a free trial?

Yes! We offer a 7-day free trial with unlimited practice, automatically activated for all new users. You can test out all features without entering your credit card. In 7 days, you'll discover whether voice-based practice builds the confidence you need for your upcoming interviews.Read more →

Master Your Data Engineering Interview

Get personalized feedback on your data pipeline design, ETL processes, and database architecture capabilities

Data Engineer Interview Overview

Don't Bomb Your Data Engineer Interview

Common Data Engineer Interview Questions

Explain the difference between ETL and ELT. When would you use each?

How would you design a data pipeline to process millions of records daily?

What is a slowly changing dimension (SCD) and how would you handle it?

How do you optimize a slow-running SQL query?

Explain the difference between batch and stream processing. When would you use each?

What is data partitioning and why is it important?

How would you ensure data quality in a data pipeline?

Explain the CAP theorem and its relevance to distributed databases.

What is Apache Spark and how does it differ from MapReduce?

Describe a data pipeline failure you encountered. How did you debug and resolve it?

How would you handle schema evolution in a data pipeline?

What is data lake vs data warehouse? When would you use each?

How would you design a real-time analytics system?

What factors do you consider when choosing between different storage formats (Parquet, ORC, Avro)?

How would you implement data lineage and metadata management?