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Welcome to Exponent’s Data Engineering Interview Course!

Data engineering interviews test how well you can write SQL under pressure, model data for real business problems, design scalable pipelines, and explain the technical decisions behind your past work. This course is built to prepare you for the full data engineering interview loop, with a focus on the rounds where candidates most often get stuck: timed SQL and Python, data modeling cases, and pipeline design.

The work is broad, and so are the interviews. A typical loop can move from a multi-table SQL query to a Kimball-style data modeling case to a partitioning tradeoffs discussion in the same hour. This course is designed to get you comfortable across all of it.

How we made this course

We collaborated with senior data engineers and leaders from top tech companies like Meta, Amazon, Databricks, and Accenture, who have extensive experience both taking and conducting data engineering interviews.

Together, we built this course to share their best practices, interview rubrics, and the key technical and problem-solving questions you'll need to master for data engineering interviews. That's why the Exponent membership includes:

  • Practice questions for all types of data engineering interview questions, including SQL, Python, data modeling, ETL design, and behavioral.
  • A dynamic, interactive format for self-guided practice.
  • Hundreds of data engineering interview lessons, questions, and answers.
  • Unique guides and frameworks for each question type.
  • Early and exclusive access to free lessons and blog posts.
  • A peer-to-peer mock interview platform to practice with other candidates.
  • Access to our Slack community for support throughout your prep.

Who is this course for

This course is for candidates applying to data engineering roles who already have a working grasp of data engineering concepts and want to demonstrate those skills under interview conditions.

The expectations vary by company, so the course is designed to give you a well-rounded foundation across SQL, data modeling, pipeline design, coding, and behavioral. You can also go deeper on specific topics, whether that's slowly changing dimensions, building scalable ETL pipelines, or SQL window functions.

Reports from data engineer candidates interviewing at companies including Meta, Amazon, Microsoft, and others show a few consistent patterns in what interviewers care about:

  • SQL is the gating skill. Candidates routinely face technical screens with three SQL questions in 25 to 30 minutes, often involving advanced window functions, CTEs, multi-table joins, and functions like LAG and LEAD. At Meta in particular, candidates report SQL is the single biggest filter.
  • Time pressure is the real test. Strong candidates lock in a working solution first, then optimize if there's time left. Practicing on a timer is what separates candidates who know SQL and Python from candidates ready to perform under interview conditions.
  • Onsite rounds are blending skills. Some loops, especially at Meta, combine product sense, data modeling, SQL, and Python in a single round rather than testing them in isolation. Candidates need to move fluidly from defining metrics to building a schema to writing the queries that support it.
  • Data modeling questions go past the schema. Interviewers push on partitioning choices, bucketing, and how the design holds up past 1 TB. Designing a Kimball-style model for an Uber, Airbnb, or e-commerce scenario is common, and the followups are where rounds are won or lost.
  • Behavioral rounds are easy to under prepare for. Multiple candidates flagged behavioral as the area they wished they had practiced more. Expect specific questions about pipeline failures, data quality issues, and tradeoffs you've made on past projects.

These shifts don't replace the fundamentals. They make them more important. Strong SQL is what lets you survive the time pressure. A clear understanding of dimension and fact table design is what lets you defend partitioning choices when the interviewer pushes on scale. The course is built to give you both: the foundations data engineering interviews have always tested, and the patterns showing up most often in current loops.

What you'll learn

This course won't teach data engineering from scratch. It focuses on practical, actionable advice for each interview stage.

By the end of the course, you'll have:

  • A clear picture of the question types you'll face and how to structure your answers.
  • The key data engineering topics worth reviewing before your loop.
  • The most common mistakes candidates make in interviews and how to avoid them.
  • Familiarity with the tools and patterns used in modern ETL pipelines.
  • Advanced topics that help you stand out in senior rounds.

Here's what the course covers:

  • Data Modeling: Design effective data models, including dimension and fact table design.
  • Data Pipeline Design: Build complex ETL and ELT systems tailored to business requirements.
  • SQL Interviews: Master SQL interview techniques, with a focus on structuring answers and writing efficient code under time pressure.
  • Data Structures and Algorithms: Sharpen your skills in solving DS and A questions by recognizing common patterns and practicing strategically.
  • Behavioral: Demonstrate leadership, resilience, and teamwork. For senior roles, this becomes especially important.

Once you've worked through the lessons, keep practicing in our interview question database. We select the top answers to give detailed feedback and analysis.

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