Introduction

After nearly two decades in the data engineering field, I’ve sat on both sides of the interview table countless times. Whether you’re a seasoned professional looking to change roles or a newcomer trying to break into the field, the interview process for data engineering positions can be both challenging and mysterious. There’s often uncertainty about what questions you’ll face, what skills you need to demonstrate, and what interviewers are really looking for beneath the surface.

Data engineering interviews are particularly complex because they sit at the intersection of software engineering, database management, data analysis, and business knowledge. A successful data engineer needs to demonstrate technical prowess while also showing they understand how data flows through an organization and drives business decisions.

In this guide, I’ll go over my experience in running data engineering interviews. I’ll share insights from my experience both as a candidate and as an interviewer, covering the different types of interviews you’ll face, preparation strategies that actually work, and the common pitfalls that trip up even experienced engineers.



The Interview Landscape

Data engineering interviews typically follow a structured path that evaluates candidates across multiple dimensions. Understanding this landscape is your first step toward successful preparation.

The Screening Call

Your journey usually begins with a screening call with a recruiter, sometimes a hiring manager. While this might seem like a formality, it’s actually a critical juncture where many candidates are filtered out. The screener is assessing basic qualifications, communication skills, and cultural fit.

During my time leading engineering teams, I’ve conducted hundreds of these calls, and the candidates who stood out were those who could concisely articulate their experience and demonstrate genuine interest in the role. Don’t underestimate this phase—it’s your foot in the door.

A common mistake I see is candidates who dive too deep into technical details without first establishing context. Remember that your screener might not have a technical background, so focus on communicating the impact of your work in terms anyone can understand.

The Technical Assessment

After clearing the initial screening, you’ll face the technical assessment. This could take several forms:

  1. Take-home assignments: These typically involve solving a real-world data engineering problem, such as designing a data model, building a simple ETL pipeline, or optimizing a query.

  2. SQL interviews: Expect to write and optimize queries of varying complexity, often with a focus on window functions, joins, and performance considerations.

  3. Coding interviews: These assess your ability to write clean, efficient code to solve data manipulation problems, usually in Python, Java, or Scala.

  4. System design interviews: You’ll be asked to design a data architecture or pipeline to solve a specific business problem, considering factors like scalability, fault tolerance, and data consistency.

I once interviewed a candidate who had impressive credentials but struggled with a basic SQL problem involving window functions. When I asked about their experience, they admitted they’d been managing a team for so long that their hands-on SQL skills had atrophied. The lesson? Always brush up on your fundamentals, regardless of your seniority.

The Behavioral Assessment

Data engineering doesn’t happen in isolation. You’ll need to demonstrate that you can work effectively with data scientists, analysts, stakeholders, and other engineers. Behavioral interviews assess your soft skills, including:

  • Communication and explanation abilities
  • Problem-solving approach
  • Teamwork and conflict resolution
  • Time management and prioritization
  • Adaptability and learning mindset

When I ask candidates about a challenging project they’ve worked on, I’m not just interested in the technical details. I want to hear how they navigated roadblocks, collaborated with others, and ultimately delivered value. Your technical skills might get you in the door, but these soft skills often determine whether you’ll thrive in the role, how adaptable you are.



SQL Mastery: The Foundation of Data Engineering Interviews

SQL remains the lingua franca of data engineering, and nearly every interview will test your proficiency with it. Based on my experience, SQL interviews generally fall into two categories:

The “Screener” SQL Interview

This shorter assessment (usually 45-60 minutes) tests your fundamental SQL skills. You’ll typically face 3-5 problems of easy to medium difficulty. The goal isn’t to test the boundaries of your knowledge but to ensure you have a solid foundation.

Common question types include:

  • Aggregating and grouping data
  • Joining multiple tables
  • Filtering with complex conditions
  • Basic window functions
  • Date and string manipulation

A candidate once told me they were surprised by how “basic” the SQL questions were in their interview. What they failed to realize was that interviewers weren’t just checking if they could write correct SQL—they were evaluating how cleanly, efficiently, and readably they wrote it. The quality of your SQL is often more important than your ability to solve complex puzzles.

The “Deep Dive” SQL Interview

For more senior positions or roles with heavy data transformation responsibilities, you might face a deep dive SQL interview. This 60-90 minute session will test not just your ability to write queries but your understanding of performance optimization, query plans, and database internals.

Expect questions on:

  • Optimizing complex queries
  • Understanding execution plans
  • Index design and selection
  • Partitioning strategies
  • Handling large datasets efficiently

During a deep dive interview I conducted, I asked a candidate to optimize a slow-running query. They immediately suggested adding indexes to every column in the WHERE clause. The strongest candidates understand the tradeoffs involved and consider factors like cardinality, query patterns, and write performance before suggesting optimizations.

Common SQL Interview Mistakes

Throughout my career, I’ve seen candidates make the same mistakes repeatedly:

  1. Jumping straight into coding: Take time to understand the problem, ask clarifying questions, and outline your approach before writing any SQL.

  2. Overusing engine-specific functions: Stick to ANSI-standard SQL unless you’re sure the interviewer is looking for engine-specific optimizations.

  3. Writing unnecessarily complex queries: Often, a simple, readable solution is better than a clever one-liner. Your code should be maintainable by others.

  4. Neglecting edge cases: What happens with NULL values? Empty sets? Duplicates? Consider these scenarios without being prompted.

  5. Forgetting to talk through your thought process: Interviewers want to understand how you approach problems, not just see the final solution.

I once interviewed a candidate who solved a complex SQL problem with an elegant window function. When I asked them to solve it without window functions (to test their fundamentals), they struggled. The lesson? Make sure you understand the underlying concepts, not just the shortcuts.



Data Modeling Interviews: Building the Blueprint

Data modeling interviews assess your ability to design schemas that balance performance, flexibility, and business needs. These interviews often involve a whiteboarding or diagramming session where you’ll design a data model for a specific scenario.

The Interview Format

A typical data modeling interview lasts 60 minutes and asks you to create schemas for 3-5 tables. You’ll need to identify entities, relationships, keys, and appropriate data types. More importantly, you’ll need to justify your design decisions and discuss tradeoffs.

I once asked a candidate to design a data model for an e-commerce system. They immediately jumped into creating tables without asking any questions about the business requirements. After they finished, I asked how their model would support analyzing customer purchasing patterns over time, a critical business need they hadn’t considered. Their model would have required expensive joins and complex queries to answer basic business questions.

Keys to Success

The strongest candidates in data modeling interviews:

  1. Ask clarifying questions before designing: Understand the business requirements, access patterns, and volume of data before putting pen to paper.

  2. Consider query patterns: A schema should make the most common queries efficient and straightforward.

  3. Discuss normalization tradeoffs: Know when to normalize for data integrity and when to denormalize for performance.

  4. Address temporal aspects: Many business questions involve time—how will your model handle historical changes?

  5. Consider scaling issues: How will your model perform as data volume grows? What partition strategies make sense?

A particularly impressive candidate started their modeling session by saying, “Before I design the schema, let me understand how this data will be used.” They then asked insightful questions about query patterns, update frequency, and business priorities. Their final design wasn’t just technically sound—it directly addressed the specific needs of the business.

Handling Tricky Questions

Interviewers often introduce complications to test your adaptability:

  • “How would your design change if we needed to support real-time analytics?”
  • “What if we need to track all historical states of this entity?”
  • “How would you modify this for a multi-tenant system?”

The best approach is to think aloud, discuss tradeoffs explicitly, and be willing to adapt your initial design. Remember that there’s rarely a single “correct” data model—the goal is to show your thought process and understanding of the implications of different design choices.



Coding Interviews: Beyond SQL

While SQL is fundamental, most data engineering roles also require proficiency in programming languages—typically Python, Java, or Scala. Coding interviews assess your ability to write clean, efficient, and maintainable code to solve data manipulation problems.

What to Expect

Coding interviews for data engineering differ from traditional software engineering interviews. Rather than focusing on algorithms and data structures (though these are still relevant), you’ll likely encounter problems related to:

  • Parsing and transforming data
  • Implementing ETL logic
  • Optimizing memory-intensive operations
  • Working with APIs and data sources
  • Implementing data validation and quality checks

I once asked a candidate to write a Python function that would process a large CSV file of transactions and identify potential duplicates based on fuzzy matching criteria. The strongest solutions demonstrated not just correct functionality but also considered memory efficiency, error handling, and edge cases.

Preparation Strategies

Based on my experience, the most effective preparation for coding interviews includes:

  1. Practicing with real-world data problems: Sites like LeetCode and HackerRank have data engineering-specific problems that are worth practicing.

  2. Reviewing common data manipulation libraries: Be comfortable with pandas, NumPy, PySpark, or equivalent libraries in your preferred language.

  3. Understanding memory and performance considerations: Know how to optimize code for large datasets that don’t fit in memory.

  4. Developing clean coding habits: Use meaningful variable names, add appropriate comments, and structure your code logically.

A candidate once impressed me not with the cleverness of their solution but with how methodically they approached the problem. They started by writing tests, then implemented a basic solution, and finally optimized it step by step. This demonstrated not just coding ability but a disciplined engineering approach.

Communication During Coding Interviews

Technical skill alone isn’t enough—you need to communicate effectively during coding interviews:

  • Verbalize your thought process as you approach the problem
  • Explain the tradeoffs you’re considering between different approaches
  • Ask clarifying questions when requirements are ambiguous
  • Discuss complexity and performance implications of your solution

The strongest candidates don’t just code silently; they treat the interview as a collaborative problem-solving session. This gives interviewers insight into not just what you know but how you work.



Behavioral Interviews: The Human Element

Technical skills might get you through the door, but behavioral skills often determine whether you’ll succeed in the role. Data engineers must collaborate with stakeholders across the organization, from business users to data scientists to software engineers.

The STAR Method

When answering behavioral questions, STAR method (Situation, Task, Action, Result) can be incredibly effective. This structured approach ensures you provide a complete picture of your experience:

  • Situation: Describe the context and background
  • Task: Explain what was required of you
  • Action: Detail the specific steps you took
  • Result: Share the outcomes and what you learned

For example, when asked about a time I had to optimize a poorly performing data pipeline:

Situation: “Our marketing analytics pipeline was taking over 8 hours to run, meaning analysts were always working with day-old data.”

Task: “I needed to reduce processing time to under 1 hour without disrupting the existing reporting workflow.”

Action: “I profiled the pipeline and identified several inefficiencies: redundant data transformations, unoptimized joins, and a lack of parallelization. I redesigned the architecture to eliminate duplicate work, rewrote key transformations to leverage partitioning, and implemented incremental processing where possible.”

Result: “The optimized pipeline completed in 45 minutes, enabling same-day decision making. The marketing team was able to adjust campaigns more responsively, resulting in a 15% improvement in campaign performance.”

This example doesn’t just state what I did—it provides context, specifics about my approach, and quantifiable results.

Common Behavioral Questions

Based on my experience, these questions frequently appear in data engineering interviews:

  1. “Tell me about a challenging data project you worked on.”
  2. “Describe a situation where you had to explain a technical concept to a non-technical stakeholder.”
  3. “How do you handle situations where requirements are unclear or changing?”
  4. “Tell me about a time you had to make a difficult decision about technical tradeoffs.”
  5. “How do you approach debugging complex issues in data pipelines?”

The best way to prepare for this, is to think of projects you have worked on in the past. Then when it comes to the interview, you will have some good ready prepared examples to use.

Demonstrating Growth Mindset

One quality I consistently look for in candidates is a growth mindset—the belief that abilities can be developed through dedication and hard work. This manifests in several ways:

  • Willingness to acknowledge mistakes and what you learned from them
  • Examples of how you’ve developed new skills or adapted to new technologies
  • Openness to feedback and how you’ve incorporated it into your work
  • Curiosity and enthusiasm for continuous learning

A candidate once shared a story about a production issue caused by their code. Instead of deflecting blame, they walked me through how they addressed the immediate problem, the root cause analysis they conducted, and the processes they implemented to prevent similar issues. This honest reflection demonstrated maturity and a commitment to growth that was far more compelling than a sanitized success story.



Preparation Strategies: A Holistic Approach

With an understanding of the different interview types, let’s discuss how to prepare effectively.

Technical Preparation

  1. SQL mastery: Ensure you can comfortably write complex queries involving joins, window functions, CTEs, and subqueries. Practice on platforms like LeetCode, HackerRank, or DataLemur.

  2. Programming practice: Solve data manipulation problems in your preferred language, focusing on efficiency and clean code.

  3. Data modeling exercises: Practice designing schemas for different scenarios, considering normalization, query patterns, and scaling requirements.

Balance your preparation across different areas rather than going too deep in one direction.

Behavioral Preparation

  1. Catalog your experiences: Create a “story bank” of projects, challenges, and accomplishments you can draw from during behavioral interviews.

  2. Practice articulating technical concepts: Ask a friend or mentor to play the role of a non-technical stakeholder and practice explaining complex ideas in simple terms.

  3. Reflect on challenges and growth: Prepare to discuss not just successes but also difficulties and what you learned from them.

A former colleague once shared how they prepared for behavioral interviews by recording themselves answering common questions. This helped them identify verbal tics, circular explanations, and areas where they needed more concrete examples.

Mock Interviews

I cannot overstate the value of mock interviews. They:

  • Simulate the pressure of real interviews
  • Provide opportunities to practice articulating your thoughts
  • Help identify weak spots in your knowledge or presentation
  • Build comfort with the interview format

Consider finding a mentor or peer in the field who can conduct mock interviews with you. Alternatively, platforms like Pramp or interviewing.io offer structured practice with feedback.

Day-of Strategies

Based on my experience both interviewing and being interviewed, these strategies help maximize performance on interview day:

  1. Physical preparation: Get adequate sleep and consider light exercise before interviews to reduce anxiety and increase mental clarity.

  2. Environmental setup: For remote interviews, ensure your space is quiet, your background is professional, and your technology is reliable.

  3. Pacing yourself: For full-day interview loops, request breaks between sessions to recharge. Even 15-30 minutes can make a significant difference.

  4. Active listening: Pay close attention to the specifics of each problem. Many candidates rush to solve what they think the question is asking rather than what it’s actually asking.

I’ve made the mistake of scheduling back-to-back interviews without breaks, only to find my performance degrading throughout the day. Now I always request at least 30 minutes between sessions to reflect, reset, and prepare for the next challenge.



Conclusion: The Meta-Interview

Beyond the specific questions and formats, data engineering interviews evaluate your ability to do three things:

  1. Solve complex technical problems: Can you apply your knowledge to new and unfamiliar challenges?

  2. Communicate effectively: Can you explain your thinking, collaborate with others, and adapt to feedback?

  3. Connect technical work to business impact: Do you understand how data engineering supports organizational goals?

The candidates who stand out are those who demonstrate not just technical proficiency but also a holistic understanding of how data flows through an organization and creates value.

As you prepare for your next data engineering interview, remember that the process isn’t just about proving what you know—it’s about showing how you think, how you learn, and how you collaborate. By approaching interviews with this mindset, you’ll not only perform better but also identify roles and teams where you’ll truly thrive.

The data engineering field continues to evolve rapidly, but these core interview principles remain consistent. Master them, and you’ll be well-positioned to succeed regardless of the specific technologies or methodologies in vogue.

Remember that each interview, regardless of outcome, is an opportunity to learn and grow.