Ace Your Data Engineering Interview (2025 Guide)

Below, we discuss how to prepare for and ace data engineering interviews.

Unlike more generalist roles like Full Stack or Backend, data engineering is a highly specialized discipline that comes with its own set of unique challenges.

You're evaluated on your general coding abilities and expertise in designing robust data pipelines and managing complex data architectures.

This guide breaks down the data engineering interview process into digestible sections—from recruiter screens to technical assessments.

Software vs. Data Engineering Interviews

At a high level, both software and data engineering interviews follow a familiar structure. You'll typically encounter rounds that include coding challenges, behavioral assessments, and a recruiter screen. 

Similarities

• Identical Rounds: Expect many of the same rounds, including coding exercises, behavioral interviews, and a recruiter screen.
• Coding Focus: Both disciplines will test your ability to write clean, efficient code under time constraints.
• Behavioral Interviews: You’ll be asked questions to understand your past experiences, problem-solving skills, and cultural fit.
• Recruiter Screen: A brief initial conversation to confirm the basics (current role, work authorization, location, etc.) and schedule subsequent rounds.

Differences

• Specialized System Design: While Software Engineering interviews might focus on traditional system design, Data Engineering interviews often delve into designing ETL pipelines and data models.
• Tailored Coding Challenges: The coding portion of data engineering might be more oriented towards SQL and data manipulation than solely complex algorithmic problems.
• Tech Stack & Big Data Focus: You could be asked technology-specific questions ranging from Big Data tools to performance optimizations, which are unique to the data landscape.
• Company Size Matters: Larger companies tend to have more predictable, specialized processes for data roles, whereas smaller companies might interview you like a typical Software Engineer.

Given these nuances, remain flexible in your interview preparation.

Data engineering interviews can vary more widely than typical SWE loops, so tailor your approach based on each company's specific expectations.

Big Tech Data Engineering

Here are some popular companies and their data engineering interview loops.

Recruiter Screen

The recruiter screen is typically a brief, low-stakes conversation designed to gather essential information and set the stage for more technical rounds.

Here's what you can generally expect:

• Overview: This round is essentially a lightweight behavioral interview. The focus is on your current role, reasons for seeking new opportunities, and logistical check-box questions, such as your work authorization, location preferences, and salary expectations.
• What They’re Looking For: Recruiters want a clear picture of your background. They’ll ask questions like "What are you up to right now?" and "Why are you interested in company X?" If you've had shorter stints at previous roles, be prepared to explain those transitions.
• Scheduling the Next Step: Often, the recruiter screen is followed by planning a technical phone screen. This may happen immediately during the call or later via email or through your career profile page.
• Taking Notes: It’s a good idea to jot down key details during the call. Pay attention to the culture and specifics about the team, the hiring manager, and the company's products, services, and tech stack. This information can be incredibly valuable for future interactions with team members and the hiring manager, as it demonstrates your genuine interest and helps you tailor your responses more effectively.

Tips for Success:

• Prepare a concise summary of your current role and accomplishments.
• Have clear, thought-out answers to common questions like "Why company X?" or "Why are you seeking new opportunities?"
• Be ready to discuss any short tenure on your resume with honest, positive explanations.
• Use the call to gather insights to inform your approach in future rounds.

This initial conversation is your first chance to make a strong impression—so be clear, be concise, and use it as an opportunity to pave the way for the upcoming technical challenges.

Technical Screen

The technical screen is typically a one-hour session to assess your hands-on coding and data manipulation abilities under time constraints.

• Coding Questions: Expect 1–3 coding problems that are usually easy-to-medium. The emphasis is on writing clear, functional code.
• SQL Questions: You might also be given 1–3 SQL questions. These problems often progress from easy to medium difficulty, testing your ability to work with data quickly and accurately.
• Dynamic Difficulty: The format can vary depending on the company and your experience level. Some interviews might present a mix—say, 1 medium coding question paired with 2 medium SQL questions, or alternatively, a series of easier problems in both categories. It's important to understand the specifics from your recruiter ahead of time.

Practical Considerations:

• Coding questions are typically tackled first, so be prepared to shift gears into SQL under time pressure.
• Clarify whether you can run your code during the interview, which can affect your approach.
• Avoid focusing exclusively on coding or SQL—both skills are critical.

Preparation Tips:

• If your interview is within the next week, consider ramping up your SQL practice. It’s often less frequently emphasized than coding.
• Working with a mock interviewer can be extremely beneficial for simulating the technical screen environment. It can help you determine the optimal number of problems to solve and whether live code execution is part of the process.
• Always check with your recruiter to clarify the interview format so you can tailor your preparation effectively.

This technical screen is your opportunity to demonstrate your ability to handle the practical challenges unique to Data Engineering roles.

Take Home

Take-home assessments are popular in the interview process for many DE roles, especially at smaller companies.

They're not as common as in data analyst or data scientist positions.

When you receive a take-home project, consider it a practical exercise and a foundation for future interview rounds.

• Exercise Caution with AI: AI tools can help clarify ambiguities or double-check your work, but avoid relying on them blindly. Understand every part of your solution; you'll likely need to explain your approach later.
• Build a Strong Foundation: Your take-home assessment often serves as the basis for later discussions in your interview process. Whatever method you choose, ensure you can confidently discuss what you did and why you made those choices.
• Time Management is Key: Start early—don’t be swayed by recruiters' or teams' optimistic time estimates. These estimates are often lower than the actual time required to complete the project to a high standard.
• Tailor Your Approach: Since smaller companies are more likely to use take-home assessments, be prepared for varied formats and expectations. Use the opportunity to showcase your practical skills and problem-solving abilities.

For more detailed guidance on tackling take-home assessments, check out our dedicated resource on this topic [link for more info on Take-Home Assessments].

Coding

The final coding round is designed to challenge your problem-solving abilities and assess your mastery of core data structures and algorithms—often in ways that differ from the technical screen.

While the technical screen might focus on a few easy-to-medium problems, the final round typically involves more in-depth coding challenges.

What to Expect

• Core Data Structures & Algorithms: Prepare to tackle problems involving Binary Search, Depth-First and Breadth-First Search, linked lists, hashmaps, and heaps. You’ll need these foundational skills to solve more complex problems efficiently.
• Varied Problem Types: Expect various questions, including array manipulations, tree or graph traversals, and dynamic programming challenges. While you might encounter around three key problems, each is designed to test your ability to optimize and iterate on your solutions.

Interview Strategy

• Clarity and Efficiency: Focus on writing clean, well-documented code.
• Edge Cases: Always consider the edge cases, as interviewers are keen on understanding your thought process around potential pitfalls.
• Practice Makes Perfect: Regularly solving problems and reviewing different approaches can make a significant difference.

Preparation Tips

• Revise the Fundamentals: Brush up on core topics and practice coding problems that require implementing or modifying classic data structures.
• Simulate the Environment: Work with mock interviews to get used to articulating your thought process while writing code.
• Understand the Trade-Offs: Be ready to discuss the efficiency and scalability of your solutions and explain why one approach might be more optimal than another.

SQL

Some companies opt for a dedicated SQL round focusing solely on your ability to query, manipulate, and analyze data.

This round can differ from the technical screen by honing in on practical database skills.

Core Topics

• JOINs & Unions: Mastering how to combine data from multiple tables.
• Aggregations & Groupings: Crafting queries that summarize data effectively.
• Subqueries & CTEs: Using nested queries and common table expressions to simplify complex operations.
• Window Functions: Analyzing data across a set of rows related to the current row.
• Performance Optimizations: Writing efficient queries and understanding indexing strategies.
• Handling Nulls & Conditional Logic: Manage missing data and apply logic directly to your SQL queries.
• Data Transformation & Pivoting: Reshaping data to fit various reporting or analytical needs.

Covering these core topics thoroughly is a course in itself. For a deeper dive into SQL, consider checking out the Data Engineer course, which explores these areas in greater detail.

SQL Interview Formats

The format of SQL interviews can vary widely from one company to another.

You might face questions that require you to write a complete query from scratch, optimize an existing query, or solve a problem that mimics real-world data challenges.

ETL Pipelines

The ETL Pipeline round is a system design exercise tailored to data pipelines.

In this segment, interviewers want to understand how you break down complex problems and design robust, scalable pipelines that handle data ingestion, transformation, and storage.

What to Expect

• Clarifying Questions: Start by asking questions to define the problem—discussing the scope, understanding key requirements, identifying critical entities, and determining the necessary API inputs.
• Pipeline Design: Once you understand the requirements clearly, walk through your design process. Outline the data flow from source to destination and explain how each component interacts within the pipeline.
• Key Components of an ETL Pipeline:
  • Queues: Tools like Kafka or SQS to manage data ingestion and buffering.
  • Distributed Compute: Technologies such as Spark, Kubernetes, or ECS clusters to handle large-scale data processing.
  • Data Warehousing: Storage solutions like Redshift, Data Lakes, or S3 for persistent data storage.
  • On-Demand Compute: Serverless options like Lambda for event-driven processing.
  • Monitoring & Governance: Tools such as Datadog or CloudWatch to ensure system reliability and performance.
  • Visualization Layer: Platforms like Tableau or Looker that enable end-users to derive insights from the processed data.
• SQL Queries for Metrics: In some cases, you might be asked to write SQL queries near the end of your design. This is typically done to extract metrics or validate the data flow and quality, adding an extra layer of practical evaluation to your pipeline design.

Follow-Up and Flexibility

Expect follow-up questions requiring you to adjust your design based on new constraints or additional requirements.

Interviewers might present scenarios—like designing a pipeline for real-time streaming data from a mobile app—to assess your ability to apply these concepts in a practical context.

This section evaluates your technical design skills and ability to communicate complex systems clearly.

Data Modeling

Data modeling is designing and structuring a data warehouse to meet business needs.

Unlike ETL pipelines, which focus on the flow and processing of data (the "how"), data modeling centers on defining what data should be stored and how it’s organized.

It’s about translating business requirements into a robust data schema that efficiently supports analytics and reporting.

What to Expect

• Understanding Business Requirements: You'll typically be presented with a scenario for a particular app or business. Your task is to break down the business problem and identify the key metrics and entities that must be captured.
• High-Level Design: Begin by decomposing the problem and outlining a high-level design. Identify the core components of the data model, including the necessary fact and dimension tables.

Key Components

• Fact Tables: These store quantitative data—like sales figures or usage metrics—and are central to your analysis.
• Dimension Tables: These provide context for facts such as time, location, customer, or product information.
• Slowly Changing Dimensions: Handling slowly changing dimensions is crucial in data modeling. For example, when a customer changes their address, you must decide whether to overwrite the old address (Type 1), add a new row to preserve history (Type 2) or store limited historical information in additional columns (Type 3). Your approach should be based on how critical maintaining historical accuracy is for the business.
• Schema Types: You may be asked to choose between models like a star or snowflake schema. Each has its own trade-offs in terms of simplicity and query performance.

Interview Process

• Problem Decomposition: Break down the given scenario into manageable parts. Identify what needs to be measured (facts) and the contextual information (dimensions) required for analysis.
• Designing the Schema: Outline the fact and dimension tables and discuss how they relate. If required, consider designing aggregate tables to speed up query performance.
• Optimization Considerations: Discuss strategies for optimizing the schema. These might include indexing strategies, denormalization, or other performance-enhancing techniques.
• Practical SQL: In some cases, you may be asked to write sample SQL queries to demonstrate how data would be extracted and analyzed from your model.

Example Scenario

For instance, you might be tasked with designing a data warehouse for an e-commerce platform.

The interview could prompt you to create a model that tracks orders, customer behavior, and product inventory.

You’d explain how you’d structure a fact table for orders, link it to dimensions such as customers, products, and time, and potentially propose aggregate tables to optimize common queries.

Be sure to address how you’d handle slowly changing dimensions, such as managing customer address updates, to ensure historical data remains accurate.

Behavioral

Behavioral interviews share many similarities with those for Software Engineering positions, yet they also feature nuances tailored to the data domain.

• General Behavioral Interviews: Companies like Google and Uber typically assess your communication skills, problem-solving approach, and how you handle real-world challenges. Expect to discuss past projects, your role in overcoming obstacles, and how you collaborate with cross-functional teams.
• Culture Fit & Product-Driven Interviews: Airbnb and Atlassian strongly emphasize ensuring you align with their unique cultures and ways of working. They often probe how you adapt to rapidly changing environments and contribute to product innovation, reflecting the collaborative and dynamic nature of their teams.
• Amazon's Integrated Leadership Principles: Instead of having a dedicated behavioral round, Amazon weaves 1–2 Leadership Principle (LP) questions into almost every interview session. Throughout each round, you’ll be expected to demonstrate customer obsession, ownership, and bias for action. Familiarize yourself with these LPs and prepare concrete examples from your experience.

Your goal is clearly articulating your experiences and connecting them directly to the skills required for a DE role.

Remember that understanding each company’s specific approach—often detailed in recruiter guidance or company-specific interview guides—will help you tailor your preparation accordingly.

Sample Questions: Big Data

Below are sample questions and answers for each key topic, which should give you an idea of what might come up during your interview.

Spark & Hadoop

Question 1: "What are the main differences between Apache Spark and Hadoop MapReduce?"

Answer: Apache Spark leverages in-memory processing to significantly speed up computations, whereas Hadoop MapReduce writes intermediate results to disk, making it slower. Additionally, Spark provides a more user-friendly API and supports diverse workloads like batch processing, streaming, machine learning, and graph processing, while MapReduce is primarily designed for batch processing.

Question 2: "When would you choose Hadoop over Spark, or vice versa?"

Answer: You might choose Hadoop when processing massive datasets that exceed available memory or require a proven, disk-based processing framework. Conversely, thanks to its in-memory capabilities, Spark is preferable for applications that demand fast, iterative processing—such as real-time analytics or machine learning.

Question 3: "How do you determine how many nodes to have in your Spark cluster?"

Answer: The optimal number of nodes depends on factors such as the size and complexity of your data, workload characteristics, and performance requirements. Consider each node's memory and CPU capacity, the number of data partitions, and whether dynamic allocation is enabled to scale resources on demand. Benchmarking your workload is often the best way to fine-tune cluster sizing.

Question 4: "Can you explain the roles of executors, tasks, and how they relate within a Spark job?"

Answer: In Spark, the driver program orchestrates the execution of a job, while executors are processes running on worker nodes that execute tasks. A task is the smallest unit of work and operates on a data partition. Executors run these tasks concurrently.

MapReduce

Question 1: "Can you explain the MapReduce programming model and its main components?"

Answer: MapReduce operates with two primary functions: the Map function processes input data to generate key-value pairs, and the Reduce function aggregates those pairs to produce summarized results. The Map phase distributes and filters data, and the Reduce phase consolidates it for the final output.

Question 2: "What are some common challenges when working with MapReduce?"

Answer: A key challenge is its reliance on disk I/O, which can significantly slow processing compared to in-memory frameworks. Additionally, optimizing MapReduce jobs often requires careful tuning of job configurations and addressing issues like data skew, where certain keys result in uneven workload distribution.

Additional Question: "How do you mitigate the issue of data skew in a MapReduce job?"

Answer: Data skew occurs when specific keys are overrepresented, causing some reducers to process more data than others. To mitigate this, you can implement a custom partitioner to balance the load across reducers, use combiners to pre-aggregate data before the shuffle phase, or even re-partition your data to distribute it more evenly.

Streaming vs. Batch Processing

Question 1: "What are the key differences between streaming and batch processing?"

Answer: Streaming processes data continuously in real-time, making it ideal for scenarios that require immediate insights, such as fraud detection. In contrast, batch processing handles large volumes of data in scheduled intervals, which works well for tasks like generating daily reports where real-time processing isn’t critical.

Question 2: "Can you provide a scenario where streaming would be preferred over batch processing?"

Answer: In a real-time fraud detection system, streaming is essential because it enables immediate analysis and response to suspicious activities. Batch processing would introduce delays, potentially allowing fraudulent transactions to go undetected until after the fact.

Additional Question: "How do you manage late-arriving data in a streaming system?"

Answer: Managing late-arriving data involves techniques like watermarking and windowing, which help define cutoff times for data to be considered on time. This approach allows you to emit results on time while incorporating delayed records—either by triggering re-aggregation or adjusting the final outputs to maintain overall data consistency.

Modern Data Lake Solutions (Iceberg & Delta Lake)

Question 1: "How do Iceberg and Delta Lake handle data versioning and schema evolution?"

Answer: Iceberg and Delta Lake support ACID transactions, maintaining data integrity even as schemas evolve. They allow you to add or modify columns without disrupting existing queries, preserving historical data while accommodating structural changes.

Question 2: "What are the advantages of using Delta Lake in a data lake architecture?"

Answer: Delta Lake offers scalable metadata handling, time travel queries (access to historical versions of data), and robust ACID transaction support. These features ensure data consistency and reliability, making managing frequent updates and maintaining high-quality analytics easier.

Additional Question: "How do Delta Lake and Iceberg handle concurrent writes to ensure data consistency?"

Answer: Both frameworks use ACID transaction support to manage concurrent writes. Delta Lake employs optimistic concurrency control and versioning to allow multiple writers without conflict. At the same time, Iceberg coordinates parallel writes through its metadata management system, ensuring that transactions remain isolated and consistent.

These examples are designed to help you prepare by helping you understand the underlying concepts and how to articulate your knowledge during the interview.

Data Engineer Interview Prep

Trying to ace your data engineering interviews is no small feat!

With a broader range of topics to master—from coding and SQL to ETL pipelines and data modeling—the interview process can be more varied and unpredictable than typical loops.

Remember, this guide offers a high-level overview designed to get you started. It’s not an exhaustive resource—it's a starting point.

• 📹 Data Engineering Interview Prep Course
• ❓Top Data Engineering Interview Questions
• 📖 Company-Specific Interview Guides
• 💬 Private 1:1 Interview Coaching