Data Engineer Interview Preparation: Complete Roadmap (2026)

Data engineering interviews are a mix of SQL, Python, system design for data pipelines, and sometimes traditional coding. The balance depends on the company, but SQL is always the heaviest part.

The typical interview structure

Most companies follow a version of this:

Recruiter screen (30 min) — Background, motivation, salary expectations.
Technical phone screen (45-60 min) — Almost always SQL. Sometimes a Python data manipulation question alongside it.
Onsite round 1: SQL deep dive (45 min) — Harder SQL than the phone screen. CTEs, window functions, query optimization, handling messy data.
Onsite round 2: Python/coding (45 min) — Data processing with Python (pandas, PySpark), or general coding. Companies vary: some test LeetCode-style problems, others give data pipeline scenarios.
Onsite round 3: System design (45 min) — Design a data pipeline, a data warehouse schema, or a real-time data processing system.
Behavioral (30-45 min) — Often combined with another round. Standard behavioral questions.

What to study

SQL (40% of the interview)

SQL is the biggest chunk. You need to be fast and accurate with:

Complex joins (self-joins, multiple joins, anti-joins)
Window functions (ROW_NUMBER, RANK, LAG, LEAD, running aggregates)
CTEs for multi-step problems
Date manipulation and time zone handling
Handling NULLs correctly
Query optimization basics (indexes, explain plans, avoiding full scans)

The SQL questions in DE interviews are harder than DS interviews. You will see messier data, more complex joins, and questions about performance.

Python (25% of the interview)

Two flavors:

Data manipulation — Reading files, transforming data, handling edge cases. If you know pandas well, you can handle most of these.
General coding — Some companies ask LeetCode-style problems. Others give data-specific problems: parse a log file, build a simple ETL pipeline, implement a data validation function.

If your target company is known for heavy coding rounds (Google, Amazon), prepare for algorithms. Otherwise, focus on practical data processing.

Data pipeline design (20% of the interview)

This is system design but scoped to data. Common questions:

Design an ETL pipeline that processes 1TB of data daily
Design a real-time analytics system
Design a data warehouse schema for an e-commerce company
How would you migrate from batch to streaming?
Design a system for data quality monitoring

You need to know the building blocks: Spark, Airflow, Kafka, data warehouses (Snowflake, BigQuery, Redshift), and when to use batch vs. streaming.

Domain knowledge (15%)

Interviewers expect you to know:

Star schema vs. snowflake schema
Slowly changing dimensions (Type 1, 2, 3)
Data modeling best practices
ACID properties and CAP theorem as applied to data systems
Common data quality issues and how to handle them

Company-specific notes

Amazon — Heavy on system design for data pipelines. They care about fault tolerance, exactly-once processing, and operational concerns. Expect Leadership Principles in every round.

Google — More theoretical. They may ask you to design internal tools or solve abstract data processing problems. Coding rounds are harder.

Meta — Focus on data warehouse design and SQL performance. They care about handling petabyte-scale data.

Startups — More practical, less theoretical. They want to know if you can build a pipeline end to end, not just talk about one.

Timeline

6-8 weeks is typical. Spend the first 2 weeks on SQL until you can solve complex problems quickly. Then split your remaining time between Python, pipeline design, and behavioral prep.

PracHub has 649 SQL questions and 268 software engineering fundamentals questions from data engineering interviews. Filter by Data Engineer role to see what companies actually ask.