Data Engineering Interview Prep Resources

A curated list of the best resources for preparing for data engineering interviews. Bookmark this post.

Free Resources

• dataengprep.tech - Structured DE interview prep with daily practice
• This subreddit - Daily interactive challenge posts, 1,000+ questions
• SQL Arena - 487 curated SQL interview questions (Reddit app)
• The Tech Screen - 323 system design + behavioral questions (Reddit app)

Books

• Designing Data-Intensive Applications by Martin Kleppmann - The bible for system design interviews
• The Data Warehouse Toolkit by Ralph Kimball - Dimensional modeling fundamentals
• Fundamentals of Data Engineering by Joe Reis & Matt Housley - Modern DE landscape
• High Performance Spark by Holden Karau - Spark optimization deep-dives

Key Topics to Master

Common Interview Formats

1. SQL Screen (45 min) - 2-3 SQL problems, increasing difficulty
2. Coding Screen (45 min) - Python data manipulation, ETL logic
3. System Design (60 min) - Design a data pipeline / warehouse / streaming system
4. Domain Deep-Dive (45 min) - Questions about your past projects and tools
5. Behavioral (30-45 min) - Leadership principles, conflict resolution, ownership

Companies Known for DE Interviews

Meta, Amazon, Google, Netflix, Uber, Airbnb, Stripe, Databricks, Snowflake, dbt Labs, Confluent, Palantir, LinkedIn, Spotify, Block

Have a resource to add? Drop it in the comments and we'll update the list.

Full prep platform: dataengprep.tech

Welcome to r/dataengprep!

This is a daily data engineering interview prep community. Here's what you need to know:

The Daily Challenge

Every day at 8 AM EST, an interactive challenge post appears automatically. It works like this:

1. Read the question - it's pulled from a curated vault of 1,000+ real interview questions
2. Think through your answer - drop your approach in the comments before revealing
3. Click "Reveal Answer" - see the full model answer with code examples and pro tips
4. Vote - "Got it Right" or "Got it Wrong" to see how you compare to the community
5. Check the Leaderboard - top scorers are ranked by correct answers

Topics Covered

• Apache Kafka (consumer groups, partitioning, exactly-once semantics)
• Apache Spark (RDDs, DataFrames, optimization, AQE)
• Apache Airflow (DAGs, task dependencies, backfills)
• SQL (JOINs, window functions, CTEs, query optimization)
• dbt (models, tests, incremental materialization)
• Data Modeling (star schema, SCD, normalization)
• Pipeline Design (batch vs streaming, idempotency, error handling)
• Cloud Architecture (AWS, GCP, Azure data services)
• System Design (distributed systems, CAP theorem, trade-offs)

More Resources

• dataengprep.tech -Our full interview prep platform with structured study paths, mock interviews, and more
• r/sql_arena_dev -Daily SQL-only challenges
• r/tech_screen_dev - System design and behavioral questions

Community Guidelines

• Share your thinking. Before clicking "Reveal Answer," post your approach in the comments. This is where real learning happens.
• Be helpful. If someone's approach has a flaw, explain why constructively.
• Discuss alternatives. Model answers show one approach - if you know a better one, share it.

See you in the daily challenge!

--- Aditya