Interview Readiness Platform

Become interview-ready for data engineering roles.

Write answers to real interview questions, get AI-scored rubric feedback, and track your improvement across topics. Built specifically for Data Engineers.

Rubric-driven scoringMock interview modeAI explanations after every answerNo credit card required
1,200+
Practice questions
54
DE topics covered
4 roles
DE · DS · DA · AE
AI-scored
Rubric feedback

How it works

Three steps from zero to interview-confident.

Pick your topics
Choose a role (DE, DS, DA) and the specific topics you want to drill — SQL, Spark, dbt, system design, and more.
Answer and get feedback
Write your answer in plain text. Our AI scores it against a rubric and shows exactly where you lost points.
Track your progress
See your mastery scores by topic, identify gaps, and use mock interview mode to simulate the real thing.

What you practice

Core skill areas that come up in every data engineering interview.

⚙️
System Design
Design data pipelines, warehouses, and architectures. Explain trade-offs with depth and clarity.
🗄️
SQL & Python
Write and review queries, explain window functions, optimise performance, and handle edge cases.
📚
Concepts & Theory
Answer questions on data modelling, orchestration, streaming, and cloud data platforms.

20 real DE interview questions

Click any question to sign up and see AI-scored rubric feedback on your answer.

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SparkAnswer this →

You have a Spark job that runs fine on 10M rows but OOMs on 500M rows. Walk me through how you'd diagnose and fix it.

SparkAnswer this →

Explain the difference between SortMergeJoin and BroadcastHashJoin in Spark. When would you force one over the other?

dbtAnswer this →

Your dbt model takes 45 minutes to run. How do you find the bottleneck and fix it?

IngestionAnswer this →

Design an incremental ingestion pipeline for a source table that has no updated_at column.

ModelingAnswer this →

What's the difference between a fact table and a dimension table? Give a concrete example for an e-commerce company.

SQLAnswer this →

Explain SCD Type 2. Write the SQL MERGE statement to implement it.

Data QualityAnswer this →

You're seeing duplicate rows in your gold-layer table. Walk through the steps to identify the root cause.

ContractsAnswer this →

What is a data contract? Who owns it, and what happens when a producer breaks it?

StreamingAnswer this →

Compare Kafka delivery semantics: at-most-once vs at-least-once vs exactly-once. When does each matter?

OrchestrationAnswer this →

Your Airflow DAG has been running for 6 hours and is stuck. What do you check first?

StreamingAnswer this →

Explain watermarks in Flink/Spark Streaming. What happens to late-arriving events?

SQLAnswer this →

Write a SQL query to find the second-highest salary in each department without using LIMIT or TOP.

StorageAnswer this →

What is partition pruning and how does Iceberg's hidden partitioning improve on Hive-style partitioning?

OrchestrationAnswer this →

You need to backfill 18 months of data. How do you do it safely without impacting production?

ModelingAnswer this →

Explain the Medallion Architecture. What goes in Bronze, Silver, and Gold layers?

SQLAnswer this →

What's the difference between RANK(), DENSE_RANK(), and ROW_NUMBER()? Give an example where they produce different results.

StreamingAnswer this →

Your Kafka consumer lag is growing. What are the possible causes and how do you fix each?

IngestionAnswer this →

Explain Change Data Capture. What are the trade-offs between log-based CDC and query-based CDC?

SparkAnswer this →

How does Spark's Adaptive Query Execution (AQE) work? What problems does it solve?

Data QualityAnswer this →

Design a data quality test suite for a payments pipeline. What tests are blocking vs. warning?

Mock Interview Mode

Simulate the real interview

Pick a level (mid / senior / staff), go through timed rounds across topics, and get a full report with hiring verdict at the end.

Example DE rounds: Data Modelling → SQL → Python → System Design

Try a mock interview →

Interview guides

Deep dives on what interviewers actually test.

All articles →

Built for data roles

100+ topics across Data Engineering, Data Science, and Data Analytics. Questions tagged by difficulty, topic, and subtopic — so you always know what you're working on.

Data EngineerData ScientistData AnalystAnalytics Engineer

Ready to level up?

Join for free. No credit card required. Start with 10 AI-scored practice sessions.