Data engineer interview questions are a major component of your interview preparation process. However, if you want to maximize your chances of landing a data engineer job, you must also be aware of how the data engineer interview process is going to unfold.
This article is designed to help you navigate the data engineer interview landscape with confidence. Here’s what you will learn:
- the most important skills required for a data engineer position;
- a list of real data engineer questions and answers (practice makes perfect, right?);
- how the data engineer interview process goes down in 3 leading companies.
As a bonus, we’ll reveal 3 common mistakes you should avoid at all costs during your data engineer interview questions preparation.
But first things first…
What skills do you need to become a data engineer?
Skills and qualifications are the most crucial part of your preparation for a data engineer position. Here are the top 5 must-have skills for anyone aiming for a data engineer career:
- Knowledge of data modeling for both data warehousing and Big Data;
- Experience in ETLs;
- Experience in the Big Data space (Hadoop Stack like M/R, HDFS, Pig, Hive, etc.);
- SQL and Python;
- Data visualization skills (e.g., Tableau or PowerBI).
If you need to improve your skillset to launch a successful career as a data engineer, you can register for the complete 365 Data Science Program today. Start with the fundamentals with our Statistics, Maths, and Excel courses, and build up step-by-step experience with SQL, Python, R, Power BI and Tableau.
What are the most common data engineer interview questions you should be familiar with?
General Data Engineer Interview Questions
Usually, interviewers start the conversation with a few more general questions. Their aim is to take the edge off and prepare you for the more complex data engineering questions ahead. Here are a few that will help you get off to a flying start.
1. How did you choose a career in data engineering?
How to answer
The answer to this question helps the interviewer learn more about your education, background and work experience. You might have chosen the data engineering field as a natural continuation of your degree in Computer Science or Information Systems. Maybe you’ve had similar jobs before, or you’re transitioning from an entirely different career field. In any case, don’t shy away from sharing your story and highlighting the skills you’ve gained throughout your studies and professional path.
"Ever since I was a child, I have always had a keen interest in computers. When I reached senior year in high school, I already knew I wanted to pursue a degree in Information Systems. While in college, I took some math and statistics courses which helped me land my first job as a Data Analyst for a large healthcare company. However, as much as I liked applying my math and statistical knowledge, I wanted to develop more of my programming and data management skills. That’s when I started looking into data engineering. I talked to experts in the field and took online courses to learn more about it. I discovered it was the ideal career path for my combination of interests and skills. Luckily, within a couple of months, a data engineering position opened up in my company and I had the chance to transfer without a problem."
2. What do you think is the hardest aspect of being a data engineer?
How to answer
Smart hiring managers know not all aspects of a job are easy. So, don’t hesitate to answer this question honestly. You might think its goal is to make you pinpoint a weakness. But, in fact, what the interviewer wants to know is how you managed to resolve something you struggled with.
“As a data engineer, I’ve mostly struggled with fulfilling the needs of all the departments within the company. Different departments often have conflicting demands. So, balancing them with the capabilities of the company’s infrastructure has been quite challenging. Nevertheless, this has been a valuable learning experience for me, as it’s given me the chance to learn how these departments work and their role in the overall structure of the company.”
3. Can you think of a time where you experienced an unexpected problem with bringing together data from different sources? How did you eventually solve it?
How to answer
This question gives you the perfect opportunity to demonstrate your problem-solving skills and how you respond to sudden changes of the plan. The question could be data-engineer specific, or a more general one about handling challenges. Even if you don’t have particular experience, you can still give a satisfactory hypothetical answer.
“In my previous work experience, my team and I have always tried to be ready for any issues that may arise during the ETL process. Nevertheless, every once in a while, a problem will occur completely out of the blue. I remember when that happened while I was working for a franchise company. Its system required for data to be collected from various systems and locations. So, when one of the franchises changed their system without prior notification, this created quite a few loading issues for their store’s data. To deal with this issue, first I came up with a short-term solution to get the essential data into the company’s corporate wide-reporting system. Once I took care of that, I started developing a long-term solution to prevent such complications from happening again.”
4. Data engineers collaborate with data architects on a daily basis. What makes your job as a data engineer different?
How to Answer
With this question, the interviewer is most probably trying to see if you understand how job roles differ within a data warehouse team. However, there is no “right” or “wrong” answer to this question. The responsibilities of both data engineer and data architects vary (or overlap) depending on the requirements of the company/database maintenance department you work for.
“Based on my work experience, the differences between the two job roles vary from company to company. Yes, it’s true that data engineers and data architects work closely together. Still, their general responsibilities differ. Data architects are in charge of building the data architecture of the company’s data systems and managing the servers. They see the full picture when it comes to the dissemination of data throughout the company. In contrast, data engineers focus on testing and maintaining of the architecture, rather than on building it. Plus, they make sure that the data available to analysts within the organization is reliable and of the necessary high quality.”
5. Can you tell us a bit more about the data engineer certifications you have earned?
How to Answer
Certifications prove to your future employer that you’ve invested time and effort to get formal training for a skill, rather than just pick it up on the job. The number of certificates under your belt also shows how dedicated you are to expanding your knowledge and skillset. Recency is also important, as technology in this field is rapidly evolving, and upgrading your skills on a regular basis is vital. However, if you haven’t completed any courses or online certificate programs, you can mention the trainings provided by past employers or the current company you work for. This will indicate that you’re up-to-date with the latest advancements in the data engineering sphere.
“Over the past couple of years, I’ve become a certified Google Professional Data Engineer, and I’ve also earned a Cloudera Certified Professional credential as a Data Engineer. I’m always keeping up-to-date with new trainings in the field. I believe that’s the only way to constantly increase my knowledge and upgrade my skillset. Right now, I’m preparing for the IBM Big Data Engineer Certificate Exam. In the meantime, I try to attend big data conferences with recognized speakers, whenever I have the chance."
Technical Data Engineer Interview Questions
The technical data engineer questions help the interviewer assess 2 things: whether you have the skills necessary for the role; and if you’re experienced with (or willing to advance in) the systems and programs utilized in the company. So, here’s a list of technical questions you can practice with.
6. Which ETL tools have you worked with? Do you have a favorite one? If so, why?
How to Answer
The hiring manager needs to know that you’re no stranger to the ETL process and you have some experience with different ETL tools. So, once you enumerate the tools you’ve worked with and point out the one you favor, make sure to substantiate your preference in a way that demonstrates your expertise in the ETL process.
“I have experience with various ETL tools, such as IBM Infosphere, SAS Data Management, and SAP Data Services. However, if I have to pick one as my favorite, that would be Informatica’s PowerCenter. In my opinion, what makes it the best out there is its efficiency. PowerCenter has a very top performance rate and high flexibility which, I believe, are the most important properties of an ETL tool. They guarantee access to the data and smoothly running business data operations at all times, even if changes in the business or its structure take place."
7. Have you built data systems using the Hadoop framework? If so, please describe a particular project you’ve worked on.
How to Answer
Hadoop is a tool that many hiring managers ask about during interviews. You should know that whenever there’s a specific question like that, it’s highly likely that you’ll be required to use this particular tool on the job. So, to prepare, do your homework and make sure you’re familiar with the languages and tools the company uses. More often than not, you can find that information in the job description. If you’re experienced with the tool, give a detailed explanation of your project to highlight your skills and knowledge of the tool’s capabilities. In case you haven’t worked with this tool, the least you could do is do some research to demonstrate some basic familiarity with the tool’s attributes.
“I’ve used the Hadoop framework while working on a team project focused on increasing data processing efficiency. We chose to implement it because of its ability to increase data processing speeds while, at the same time, preserving quality through its distributed processing. We also decided to implement Hadoop because of its scalability, as the company I worked for expected a considerable increase in its data processing needs over the next few months. In addition, Hadoop is an open-source network which made it the best option, keeping in mind the limited resources for the project. Not to mention that it’s Java-based, so it was easy to use by everyone on the team and no additional training was required.”
8. Do you have experience with a cloud computing environment? What are the pros and cons of working in one?
How to Answer
Data engineers are well aware that there are pros and cons to cloud computing. That said, even if you lack prior experience working in cloud computing, you must be able to demonstrate a certain level of understanding of its advantages and shortcomings. This will show the hiring manager that you’re aware of the present technological issues in the industry. Plus, if the position you’re interviewing for requires using a cloud computing environment, the hiring manager will know that you’ve got a basic idea of the possible challenges you might face.
“I haven’t had the chance to work in a cloud computing environment yet. However, I have a good overall idea of its pros and cons. On the plus side, cloud computing is more cost-effective and reliable. Most providers sign agreements that guarantee a high level of service availability which should decrease downtimes to a minimum. On the negative side, the cloud computing environment may compromise data security and privacy, as the data is kept outside the company. Moreover, your control would be limited, as the infrastructure is managed by the service provider. All things considered, cloud computing could be both right or wrong choice for a company, depending on its IT department structure and the resources at hand.”
9. In your line of work, have you introduced new data analytics applications? If so, what challenges did you face while introducing and implementing them?
How to Answer
New data applications are high-priced, so introducing such within a company doesn’t happen that often. Nevertheless, when a company decides to invest in new data analytics tools, this could turn into quite an ambitious project. The new tools must be connected to the current systems in the company, and the employers who are going to use them should be formally trained. Additionally, maintenance of the tools should be administered and carried out on a regular basis. So, if you have prior experience, point out the obstacles you’ve overcome or list some scenarios of what could have gone wrong. In case you lack relevant experience, describe what you know about the process in detail. This will let the hiring manager know that, if a problem arises, you have the basic know-how that would help you through.
“As a data engineer, I’ve taken part in the introduction of a brand-new data analytics application in the last company I’ve worked for. The whole process requires a well-thought-out plan to ensure the smoothest transition possible. However, even the most careful planning can’t rule out unforeseen issues. One of them was the high demand for user licenses which went beyond our expectations. The company had to reallocate financial resources to obtain additional licenses. Furthermore, training schedules had to be set up in a way that doesn’t interrupt the workflow in different departments. In addition, we had to optimize our infrastructure, so that it could support the considerably higher number of users.”
10. What is your experience level with NoSQL databases? Tell me about a situation where building a NoSQL database was a better solution than building a relational database.
How to Answer
There are certain pros and cons of using one type of database compared to another. To give the best possible answer, try to showcase your knowledge about each and back it up with an example situation that demonstrates how you have applied (or would apply) your know-how to a real-world project.
“Building a NoSQL database can be beneficial in some situations. Here’s a situation from my experience that first comes to my mind. When the franchise system in the company I worked for was increasing in size exponentially, we had to be able to scale up quickly in order to make the most of all the sales and operational data we had on hand.
But here’s the thing. Scaling out is the better option, compared to scaling up with bigger servers, when it comes to handling increases data processing loads. Scaling out is also more cost-effective and it’s easier to accomplish through NoSQL databases. The latter can deal with larger volumes of data. And that can be crucial when you need to respond quickly to considerable shifts in data loads in the future. Yes, it’s true that relational databases have better connectivity to various analytics tools. However, as more of those are being developed, there’s definitely a lot more coming from NoSQL databases in the future. That said, the additional training some developers might need is certainly worth it.”
By the way, if you’re finding this answer useful, consider sharing this article, so others can benefit from it, too. Helping fellow aspiring data engineers reach their goals is one of the things that make the data science community special.
11. What’s your experience with data modeling? What data modeling tools have you used in your work experience?
How to Answer
As a data engineer, you probably have some experience with data modeling. In your answer, try not only to list the relevant tools you have worked with, but also mention their pros and cons. This question also gives you a chance to highlight your knowledge of data modeling in general.
“I’ve always done my best to be familiar with the data models in the companies I’ve worked for, regardless of my involvement with the data modeling process. This is one of the ways I gain a deeper understanding of the whole system. In my work experience, I’ve utilized Oracle SQL Developer Data Modeler to develop two types of models. Conceptual models for our work with stakeholders, and logical data models which make it possible to define data models, structures and relationships within the database.”
Behavioral Data Engineer Questions
Behavioral data engineer interview questions give the interviewer a chance to see how you have handled unforeseen data engineering issues or teamwork challenges in your experience. The answers you provide should reassure your future employer that you can deal with high-pressure situations and a variety of challenges. Here are a few examples to consider in your preparation.
12. Data maintenance is one of the routine responsibilities of a data engineer. Describe a time when you encountered an unexpected data maintenance problem that made you search for an out-of-the-box solution".
How to Answer
Usually, data maintenance is scheduled and covers a particular task list. Therefore, when everything is operating according to plan, the tasks don’t change as often. However, it’s inevitable that an unexpected issue arises every once in a while. As this might cause uncertainty on your end, the hiring manager would like to know how you would deal with such high-pressure situations.
“It’s true that data maintenance may come off as routine. But, in my opinion, it’s always a good idea to closely monitor the specified tasks. And that includes making sure the scripts are executed successfully. Once, while I was conducting an integrity check, I located a corrupt index that could have caused some serious problems in the future. This prompted me to come up with a new maintenance task that prevents corrupt indexes from being added to the company’s databases.”
13. Data engineers generally work “backstage”. Do you feel comfortable with that or do you prefer being in the “spotlight”?
How to Answer
The reason why data engineers mostly work “backstage” is that making data available comes much earlier in the data analysis project timeline. That said, c-level executives in the company are usually more interested in the later stages of the work process. More specifically, their goal is to understand the insights that data scientists extract from the data via statistical and machine learning models. So, your answer to this question will tell the hiring manager if you’re only able to work in the spotlight, or if you thrive in both situations.
“As a data engineer, I realize that I do most of my work away from the spotlight. But that has never been that important to me. I believe what matters is my expertise in the field and how it helps the company reach its goals. However, I’m pretty comfortable being in the spotlight whenever I need to be. For example, if there’s a problem in my department which needs to be addressed by the company executives, I won’t hesitate to bring their attention to it. I think that’s how I can further improve my team’s work and reach better results for the company.”
14. Do you have experience as a trainer in software, applications, processes or architecture? If so, what do you consider as the most challenging part?
How to Answer
As a data engineer, you may often be required to train your co-workers on the new processes or systems you’ve created. Or you may have to train new teammates on the already existing architectures and pipelines. As technology is constantly evolving, you might even have to perform recurring trainings to keep everyone on track. That said, when you talk about a challenge you’ve faced, make sure you let the interviewer know how you handled it.
“Yes, I have experience training both small and large groups of co-workers. I think the most challenging part is to train new employees who already have significant experience in another company. Usually, they’re used to approaching data from an entirely different perspective. And that’s a problem because they struggle to accept the way we handle projects in our company. They’re often very opinionated and it takes time for them to realize there’s more than one solution to a certain problem. However, what usually helps is emphasizing how successful our processes and architecture have proven to be so far. That encourages them to open their minds to the alternative possibilities out there.”
15. Have you ever proposed changes to improve data reliability and quality? Were they eventually implemented? If not, why not?
How to Answer
One of the things hiring managers value most is constant improvements of the existing environment, especially if you initiate those improvements yourself, as opposed to being assigned to do it. So, if you’re a self-starter, definitely point this out. This will showcase your ability to think creatively and the importance you place on the overall company’s success. If you lack such experience, explain what changes you would propose as a data engineer. In case your ideas were not implemented for reasons such as lack of financial resources, you can mention that. However, try to focus on your continuous efforts to find novel ways to improve data quality.
“Data quality and reliability have always been a top priority in my work. While working on a specific project, I discovered some discrepancies and outliers in the data stored in the company’s database. Once I’ve identified several of those, I proposed to develop and implement a data quality process in my department’s routine. This included bi-weekly meetups with coworkers from different departments where we would identify and troubleshoot data issues. At first, everyone was worried that this would take too much time off their current projects. However, in time, it turned out it was worth it. The new process prevented the occurrence of larger (and more costly) issues in the future."
16. Have you ever played an active role in solving a business problem through the innovative use of existing data?
How to Answer
Hiring managers are looking for self-motivated people who are eager to contribute to the success of a project. Try to give an example where you came up with a project idea or you took charge of a project. It’s best if you point out what novel solution you proposed, instead of focusing on a detailed description of the problem you had to deal with.
“In the last company I worked for, I took active part in a project that aimed to identify the reason’s for the high employee turnover rate. I started by closely observing data from other areas of the company, such as Marketing, Finance, and Operations. This helped me find some high correlations of data in these key areas with employee turnover rates. Then, I collaborated with the analysts in those departments to gain a better understanding of the correlations in question. Ultimately, our efforts resulted in strategic changes that had a positive influence over the employee turnover rates.”
17. Which non-technical skills do you find most valuable in your role as a data engineer?
How to Answer
Although technical skills are of major importance if you want to advance your data engineer career, there are many non-engineering skills that could aid your success. In your answer, try to avoid the most obvious examples, such as communication or interpersonal skills.
“I’d say the most useful skills I’ve developed over the years are multitasking and prioritizing. As a data engineer, I have to prioritize or balance between various tasks daily. I work with many departments in the company, so I receive tons of different requests from my coworkers. To cope with those efficiently, I need to put fulfilling the most urgent company needs first without neglecting all the other requests. And strengthening the skills I mentioned has really helped me out.”
Interviewers use brainteasers to test both your logical and creative thinking. These questions also help them assess how quickly you can resolve a task that requires an out-of-the-box approach.
18. You have eight balls of the same size. Seven of them weigh the same, and one of them weighs slightly more. How can you find the ball that is heavier by using a balance and only two attempts at weighing?
You can put six of the balls on the balance. If one of the sides is heavier you will know that the heavier ball is on that side. If not, the heavier ball is among the two that you did not measure and it will be really easy to determine precisely which ball is heavier with your second weighing.
After you determine which side is heavier, you will have 3 balls left to choose from. You have another attempt at weighing left. You can put two of the balls on the balance and see if one of them is heavier. If it is, then you have found the heavier ball. If it is not, then the third ball is the one that is heavier.
19. A windowless room has three light bulbs. You are outside the room with 3 switches, each of them controlling one of the light bulbs. If you were told that you can enter the room only once, how are you going to tell which switch controls which light bulb?
You have to be creative in order to solve this one. You switch on two of the light bulbs and then wait for 30 minutes. Then you switch off one of them and enter the room. You will know which switch controls the light bulb that is on. Here is the tough part. How are you going to be able to determine which switch corresponds to the other two light bulbs? You will have to touch them. Yes. That’s right. Touch them and feel which one is warm. That will be the other bulb that you had turned on for 30 minutes.
You will be in serious trouble if the interviewer says that the light bulbs are LED (given that they don’t emit heat).
Although guesstimates aren’t an obligatory part of the data engineer interview process, many interviewers would ask such a question to assess your quantitative reasoning and approach to solving complex problems. Here’s a good example.
20. How many gallons of white house paint are sold in the US every year?
Find the number of homes in the US: Assuming that there are 300 million people in the US and the average household contains 2.5 people then we can conclude that there are 120 million homes in the US.
Number of houses: Many people live in apartments and other types of buildings different than houses. Let’s assume that the percentage of people living in houses is 50%. Hence, there are 60 million houses.
Houses that are painted in white: Although white is the most popular color, many people choose different paint colors for their houses or do not need to paint them (using other types of techniques in order to cover the external surface of the house). Let’s hypothesize that 30% of all houses are painted in white, which makes 18 million houses that are painted in white.
Repainting: People need to repaint their houses after a given amount of years. For the purposes of this exercise, let’s hypothesize that people repaint their houses once every 9 years, which means that every year 2 million houses are repainted in white.
I have never painted a house, but let’s assume that in order to repaint a house you need 30 gallons of white paint. This means the total US market for white house paint is 60 million gallons.
What is the data engineer interview process like?
A phone screen with a recruiter or a team member? How many onsite interviews you should be ready for? Will there be one or multiple interviewers?
Short answer: It depends on the company, its hiring policy and interviewing approach.
That said, here is what you can expect from a data engineer job interview at three top companies – Yahoo, Facebook, and Walmart. We believe these overviews will give you a good initial idea of what happens behind the scenes.
Generally, Yahoo recruit candidates from the top 10-20 schools. However, you can still get a data engineer interview through large job search platforms, such as Indeed.com and Glassdoor. Or, if you are lucky enough – with an internal referral. Anyhow, once you make the cut, you can expect a phone screen with a manager or a team lead. What about the onsite interviews? Usually, you’ll interview with 6-7 data engineer team members for about 45 minutes each. Each interview will focus on a different area, but all of them have a similar structure. A short general talk (5 minutes), followed by a coding question (20 minutes) and a data engineering question (20 minutes). The latter will often tap into your previous experience to solve a current data engineering issue the company is experiencing.
In the end, you’ll have a more general talk with a senior employee. At the same time, the interviewers will gather to share their feedback on your performance and check in with the hiring manager. If you’ve passed the data engineer interview with flying colors, you could get a decision on the day of the interview! However, if a few days have passed and you haven’t received an answer, don’t be shy to send HR a polite update request.
Usually, the data engineering interviewing process starts with an email or a phone call with a recruiter, followed by a phone screen or an in-person interview. The screening interview is conducted by a coworker and takes about 1 hour. It consists of SQL questions and online test coding tasks that you have to solve through a collaborative editor (CoderPad) in a programming language of your choice. Also, prepare to answer questions related to your resume, skills, interests, and motivation. If those go well, they'll invite you to a longer series of interviews at the Facebook office - 5 hours of in-person interviews, including a 1-hour lunch interview.
Three of the onsite interviews are focused on problem-solving. You’ll be questioned about data engineering issues that the company is facing and how you can help them solve them, for example, how to identify the metrics for performance for this specific feature) and you will be expected to write SQL and actual code for the context of the problem itself. There is also a behavioral interview portion, asking you about your work experience, and how you deal with interpersonal problems. Finally, there is an informal lunch conversation where you can ask about the work culture and other day-to-day questions.
What’s typical of Facebook interviews is that many data engineer interview questions focus on a deep understanding of their product, so make sure you demonstrate both knowledge and genuine interest in the data engineer job.
Once the interviews are over, everyone you’ve interviewed with compare notes to decide if you’ll be successful in the data engineer role. Then all left to do is wait for your recruiter to contact you with feedback from the interview. Or, if you haven’t heard from a company rep within a week or so, take matters into your own hands and send a kind follow-up email.
The data engineer interview process will usually start with a phone screen, followed by 4 technical interviews (expect some coding, big data, data modeling, and mathematics) and 1 lunch interview. More often than not, there is one more data engineer technical interview with a hiring manager (and guess what - it involves some more coding!). Anything specific to remember? Yes. Walmart has been utilizing huge amounts of big data, even before it was coined as “big”. MapReduce, Hive, HDFS, and Spark are all used internally by their data science and data engineering teams. That said, a little bit of practice every day goes a long way. And, if you diligently prepare for some coding and big data questions, you have every chance of becoming a data engineer in the world’s biggest retail corporation.
What common mistakes to avoid in your data engineer interview questions preparation?
We know that sometimes the devil’s in the details. And we wouldn’t want you to miss a single detail that could cost you your success! So, here are 3 common mistakes you should definitely refrain from making:
Not practicing behavioral data engineer interview questions
Even if you have the technical part covered, that doesn’t necessarily mean smooth sailing! Behavioral questions are becoming increasingly important, as they tell the interviewer more about your personality, how you handle conflicts and problematic work situations. So, remember to prepare for those by rehearsing some relevant stories from your past experience and getting familiar with the behavioral data engineer interview questions we’ve listed.
Skipping the mock interview
Are you so deep into your interview preparation process that you’ve cut all ties with the outside world? Big mistake! Snap out of it now, call a fellow data engineer and ask them to do a mock interview with you. Every interview has a performance side to it, and just imagining how you’re going to act or sound wouldn’t give you a realistic idea. So, while you’re doing the mock interview, pay special attention to your body language and mannerisms, as well as to your tone of voice and pace of speech. You’ll be amazed by the insight you’re going to get!
There’s one more thing you should remember about interviews. Once you pass the easier problems, you’re bound to get to the harder data engineer interview questions. But no matter how difficult they seem, don’t give up. Stay cool, calm, and collected, and don’t hesitate to ask for guidance or additional explanations. If anything, this will prove two things: that you’re not afraid of challenging situations; and you’re willing to collaborate to find an efficient solution.
Now that you’re well-familiar with the data engineer interview questions and the most important things to remember about the interview process itself, you should be much more confident in your interview preparation for that position. If you’re eager to explore more data engineer interview questions, follow the link to our all-comprising article Data Science Interview Questions. However, if you feel that you lack some of the essential skills required for the job, check out the complete Data Science Program. In case you aren’t sure if you want to turn your interest in data science into a full-fledged career, we also offer a free preview version of the Data Science Program. You’ll receive 12 hours of beginner to advanced content for free. It’s a great way to see if the program is right for you.