How to Prepare for a Data Engineering Interview in 2024

The data engineer interview preparation is so difficult because there’s a wide range of subjects that can come up. You can expect everything from advanced SQL window functions, to system design case studies.

That’s why it’s essential that you narrow down what you plan to study, and build an interview prep plan that’s built for the role. Here’s a simple study process for data engineering interviews:

• Research the format - Find out how the company conducts data engineering interviews through interview guides and forums.
• Define the topics you should study - Mine the job description and interview experiences for topics and concepts to study.
• Practice with real questions - Practice as many real data engineer interview questions as possible based on your prior research.
• Schedule mock interviews - Work with a peer, colleague or data science coach to simulate in-person interviews.

It’s an intensive process, especially if this is your first data engineering interview, or if you haven’t interviewed in a while.

To help, we’ve put together this guide, which features everything you need to know about how to prepare for a data engineering interview. This includes topics to cover, how to structure your study time, what data engineer interviews are like, and much more.

Before you even look at a SQL question, you should prepare by thinking about the role and the company. Familiarize yourself with the organization’s technology stack and company values so you can create a targeted study plan.

In your research, aim to learn more about the following:

Research the Role

Consider the job function of the role—generalist, pipeline-centric, or database-centric. Some data engineering roles are heavy on software engineering, data analytics, or data science, with some data engineering duties (e.g. building ETL pipelines).

Ask the recruiter if you’re unsure. But you can usually tell by examining the job description for specific skills and keywords.

Understand Business Goals

What does the organization hope to gain by hiring a data engineer? Knowing this will help you craft your unique value proposition as a candidate.

Plus, it’s important to keep business goals in mind when working with data. Data engineers  need to understand how to optimize data retrieval and develop dashboards, reports, and other visualizations for stakeholders.

Company’s Data Maturity

Think about the company’s size and data maturity. Large organizations typically employ a team of data scientists and/or data analysts to help understand data, so a data engineer is more likely to be database-centric (working with data warehouses across multiple databases and developing table schemas).

In a smaller company, a “generalist” data engineer may also assume data scientist responsibilities such as data analysis, machine learning, data visualization, and communicating findings to executive management.

Interview Experiences

There are plenty of interview guides and experiences on sites like Blind that can help you understand what gets asked and how the interview is structured. For example, our Facebook Data Engineer Interview guide shows the structure and types of questions that get asked in data engineering interviews at Meta.

Also, be sure to look at interview experiences on Interview Query for real-world advice and example questions.

Create a study plan that works for you, given your timeframe, the role you’re applying for, and your prior knowledge. Ideally, you’ll have at least 30 days to prepare.

Here’s how to structure your study time:

Get a baseline of your knowledge

There are various ways to validate your knowledge, including working on an end-to-end data engineering project and observing where you get stuck, or going through a list of data engineer interview questions to see how many you can answer. Find your weak areas and concentrate on those.

Create a study schedule

Identify the most important skills from the job description and create a list of your strengths and weaknesses. Prioritize important skills for the role, as well as where you struggle.

Practice as many real questions as possible

Interview Query’s real questions bank offers practice problems in a range of subjects, including SQL, Python, database design. Practice as many questions as you can.

Conduct 2-3 Mock interviews

Work with your peers, a data science coach, or colleagues who are in data engineering or data science roles. If you have enough time, do some mock interviews early in the process, which will give you a baseline, and then again closer to the interview date.

Create a Study Plan for the Interview

There is no comprehensive study curriculum for data engineering interviews. Instead, it’s about stitching together different resources so you can brush up on your weak areas. Make a checklist of things you need to know based on the role and the organization’s technology stack.

You can usually mine the job description for the most relevant skills for the interview. Essential technical skills you might study include:

• Database systems (SQL and NoSQL)
• Programming languages (Scala, Python, R, C++, Java)
• Automation tools such as Apache Airflow
• Data processing frameworks such as Apache Kafka
• Data warehousing solutions including AWS, Azure, and GCP
• ETL tools such as AWS Glue, Talend Data Integration, and Oracle Data Integrator
• Knowledge of data structures and algorithms

Some roles are heavy on statistics, while others emphasize system design or programming. For example, Amazon data engineer interviews tend to be database design-heavy, while Netflix interviews are code-heavy, “with the expectation that you can not only write SQL and code but optimize them,” according to Better Programming.

Structure your study plan around core data engineering concepts like coding, relational and non-relational databases, ETL systems, data storage, automation and scripting, machine learning, cloud computing, and data security.

You’ll need to demonstrate experience working with cloud services, understand the difference between SQL and NoSQL and how to work with both, and have knowledge of ETL tools. While Java is important for working with Big Data, stats show that over 70% of jobs based on data engineering require knowledge of the Python programming language.

Here’s a look at the most important concepts to study for data engineering interviews:

1. Algorithms & Data Structures

What to expect: You’ll need to demonstrate knowledge of basic operations such as searching, inserting, and appending, which are essential for data manipulation processes.

Beyond that, you should know how to use lists and dictionaries and how to link them. Be prepared to discuss algorithms you’ve used in previous projects, justify why you chose a particular algorithm, explain the scalability of the algorithm if used on a larger dataset, and what the outcome was.

While data engineers don’t necessarily write algorithms, a basic knowledge of algorithms is necessary for understanding the organization’s overall data function. But be prepared: During a coding interview, you may be asked to implement complex algorithms using the most efficient data structures.

Types of questions to study:

• What are some primitive data structures in Python? What are some user-defined data structures?
• What is data smoothing? How does it work?
• What is an array? What is a multidimensional array?
• What is a linked list?
• What is a hashmap in data structure?
• Given a string, determine whether any permutation of it is a palindrome
• Write a Python function called longest_increasing_subsequence that takes a list of integers as input and returns as output the length of the longest increasing subsequence in the list.

2. System Design

What to expect: System design is the most important and difficult component of the technical interview process. In a system design interview, you will design a data solution from end to end, which is usually composed of three parts:

• Data storage
• Data processing
• Data modeling

You must know what each system is best used for and its scalability. Sometimes, the initial interview question will be extremely open-ended, e.g. “Design a data warehouse from end to end.” And you’re responsible for asking follow-up questions to understand the requirements, use cases, and constraints.

Focus on constraints like requests per second, request types, data written per second, data read per second. The main challenge is to choose the best combination of data storage systems and data processing frameworks based on those requirements.

Types of questions to study:

• What is horizontal and vertical scaling?
• What are various load balancing algorithms?
• What are various cache eviction policies?
• What is the advantage/disadvantage of adding an index to a database?
• How would you create a schema to represent client click data on the web?
• Design a database to represent a Tinder-style dating app. What does the schema look like?
• For “Slack for School,” identify the key entities and their relationships. Then, design an ETL process to track student interactions, and write a query to analyze assignment trends over the past six months.

3. SQL

What to expect: Writing SQL queries is the most important data engineering skill. This means not just INSERT, DELETE, WHERE statements but things like Window functions, subqueries, CTEs (common table expressions), and how to use joins to answer questions.

You’ll be asked about SQL techniques and problem-solving approaches. In addition to using it for querying databases, SQL is regularly used as a processing pattern with various big data frameworks such as KafkaSQL, SparkSQL, Python Libraries, etc. You’ll need to know data modeling as well, which is closely related to SQL and is considered an essential part of the overall system design process.

A good data engineer should know how to translate complex business questions into SQL queries and data models. You may be given a table of SQL definitions and data and asked to construct a query, or asked how you would go about migrating data from NoSQL to SQL.

Types of questions to study:

• What are aggregate functions in SQL?
• How would you find duplicates using a SQL query?
• What is the difference between DELETE and TRUNCATE statements?
• What are the different kinds of joins in SQL?
• What is the difference between IN and BETWEEN operators?
• What is meant by normalization in SQL?
• Given a table of transactions and a table of users, write a query to determine if users tend to order more to their primary address versus other addresses.

Check out our guide to data engineer SQL questions.

Practical experience of applying core concepts is essential to acing your interview. Hackathons, competitions, and online test banks are a great way to test your knowledge.

Also, be sure to take a look at our guide 100+ Data Engineer Interview Questions. Here’s how you can study:

• Practice SQL questions and focus on medium questions and work your way up to advanced questions. Most coding interviews are conducted on whiteboards, so be sure you can nail the syntax by hand.
• Read Data Engineering Cookbook and answer at least 50 questions. This book is great for brushing up on theoretical concepts (eg: when to use a data warehouse versus a data lake) as well as learning advanced engineering skills such as Hadoop and Apache Spark. You might also check out data engineering books for more help.
• Participate in hackathons on Kaggle or HackerRank. Useful tip: Kaggle competitions are purportedly heavy on data engineering— even the ones that are meant for data scientists.
• Create your own project using publicly available data from repositories like Stackoverflow or Kaggle. Select a substantially large dataset (10GB+), import the data into a local database, try different types of analysis, and build dashboards. Ideally, your project showcases one or more crucial skills such as building a data warehouse, performing data modeling using a streaming platform, and building and organizing data pipelines.

Mock interviews help you simulate the experience, and build your confidence for the interview. At a minimum, plan for 1-2 peer-to-peer interviews. Other options include working with colleagues, or finding a data science coach who can help you conduct mock interviews.

Mock interviews will help you:

• Understand where you struggle. Mock interviews will show you which types of questions give you the most trouble. Use this information to tailor your study plan.
• Build confidence. Interviews can be nerve-wracking. Practicing with mock interviews will help you feel more comfortable in the interview room.
• Learn how to answer questions. Data engineer interviews have a particular language. Mock interviews help you practice using frameworks to answer multi-part questions.

To ace your data engineering interview, you’ll need to understand the typical interview process, the types of questions you’ll be asked, and what specific technical skills your employer is looking for.

In addition to studying for the technical portion of the interview process, you will be expected to demonstrate soft skills including communication, business acumen, and problem-solving.

Make a script for your HR phone screen

Think of the phone screen as a series of elevator pitches—short one- to two-minute responses that tell a story using the STAR format. STAR represents an interviewing technique—short for Situation, Task, Action, Result—that enables candidates to answer open-ended questions in a concise, outcome-focused format that highlights their problem-solving abilities.

Consider preparing a script for behavioral questions that are difficult to answer on the fly, like “Tell me about a time when you demonstrated good data sense” or “What do you think are the three best qualities that great data engineers share?” Research the company values and adjust your answers accordingly.

Work on product sense questions

At companies like Facebook or Netflix, which rely heavily on data, data engineers play a key role in product development strategy. Consequently, you’ll need to demonstrate strong product awareness and the ability to hold strategic conversations about the product.

Expect questions such as “What would you change about X product?” or “Design an experiment to test whether a certain feature generates user engagement.”

Create stories for portfolio review

Some companies will ask for a portfolio walk-through upfront. Prepare a story for each project in your portfolio that explains the problem statement, your selected data source(s), and the tooling decisions you made in the context of business requirements.

You should also be prepared to discuss the following:

• What trade-offs you weighed during system design?
• Why did you choose one data model or processing framework over another?
• Finally, discuss the outcome of your project. What did you learn about the data? What improvements would you make?

Anticipate what interviewers are looking for

Employers are searching for data engineers who can handle large datasets and build scaleable systems— professionals with experience in critical data functions such as managing data warehouses, building data pipelines, or working with streaming data like Spark or Kafka. Proficiency in multiple cloud platforms like AWS, Azure and GCP is one of the best ways to stand out, according to a recent survey by hiring firm Burtch Works. Finally, interviewers want to see evidence of analytical skills, problem-solving ability, communication, and culture fit.

Data engineer interviews typically follow the same template, starting with two phone screens (one behavioral, one technical), a take-home exam, followed by onsite interviews. Onsite interviews typically include multiple sessions that focus on technical skills specific to the organization’s technology stack, case studies, and culture.

Yet, there are nuances at each company. Be sure you research how companies conduct their interviews. Check out these guides to Amazon and Microsoft.

Here’s a look at the general structure of data engineering interviews:

• HR screen (15-30 minutes)

The initial screen is typically conducted by a non-technical recruiter. Expect questions about your career goals, interest in the position, and salary expectations. When describing past projects, explain the design process, what trade-offs you made, and the impact of your work. Hiring managers want to understand your thought process—not just hear a laundry list of tools you used.

• Technical screen (30-60 minutes)

Technical screens test your baseline technical ability—basic programming knowledge, familiarity with data structures and algorithms, and the ability to break down complicated ideas into workable pieces.

The interviewer, who is usually a data professional, will inquire about your prior experience with questions like “Which ETL tools have you worked with?” or “What is your experience level with NoSQL?” to qualify your experience level for the role. You’ll also be tested on your knowledge of data structures, data modeling, ETL, and so on. Expect 1-2 medium SQL questions, as well.

• Take-home exam (24 hours)

Take-home tests assess your ability to perform real on-the-job duties. Questions can be clear with explicit evaluation metrics, or they may be open-ended.

A coding challenge works like this: You’ll receive a GitHub link with a problem statement in a ReadMe file. Your job is to read the input files, do some data processing, and write the results in the output files. You will be expected to use the most optimal data structures and algorithms possible, which may involve implementing solutions using popular open-source libraries such as Spark and Pandas.

A more structured exam might require you to write code to answer 1-10 questions on 1-3 datasets, push it to your GitHub repository, and submit the link.

Here’s what hiring managers are looking for:

1. Your ability to write clean, production-ready code using programming best practices
2. Your problem-solving approach and ability to communicate it
3. Knowledge of basic data structures such as lists, arrays, dictionaries, sets, trees, and heaps
4. Your ability to decide which data structures to use and why, and to justify any trade-offs or design decisions.

• Onsite interviews

The final round consists of 2-4 technical interviews (45-60 minutes each) and an HR round. Technical interviews may consist of writing ETL code using SQL, Python or Java, answering data modeling questions based on business scenarios, giving definitions of core DE concepts, or doing whiteboard challenges on data structures and algorithms or system design.

Alternatively, you might be required to do a case study interview, where you are asked scenario-based questions that deal with architecture or a data engineering problem. Your task is to brainstorm solutions and walk the interviewer through a hypothetical design and build of the solution.

Hiring data engineers? Use OutSearch.ai’s AI platform to find candidates who excel in technical and problem-solving skills. Simplify your recruitment and meet your ideal candidates today.

Learn everything you need to know about Data Engineering with the help of this course:

If you have a data engineering coming up, you can practice with these resources from Interview Query:

• 50+ Data Engineer Interview Questions
• 15 Data Engineer Python Questions
• Top 30 Data Engineering Manager Interview Questions
• IQ’s Data Engineer Course