15+ Top Data Engineering Interview Questions and Their Answers
Data engineering is one of the fields that is emerging as the most demanding role in the tech industry. These professions draw a lot of interest because they are in great demand, pay well, and have promising long-term job development.
Be proud of how far you’ve come in your data engineering journey as you get ready for an upcoming interview. Don’t be disheartened if it takes longer than you anticipated to find a job in big data because of the intense competition; some job seekers report applying for hundreds of positions before they are even called in for an interview. When you do, to get the position, you’ll need to briefly explain why and how you used specific data methodologies and algorithms in a prior project.
Big data job interviews frequently emphasise technical rather than behavioral interview questions. These are some possible general, procedural, and technical questions for your data engineer interview.
Introductory questions
1 – Tell me about yourself
Although applicants in every field are asked this question, it is particularly significant for data engineers because you represent what you do. Even seasoned interviewees may find it challenging to answer these open-ended questions if they are preparing. Highlights should include recent successes, qualifications pertinent to the job or industry, and a description of your current situation. It could look something like this:
“Over the past two years, I have worked to refine my skills and push the envelope in the data engineering field. One of my most recent tasks was collecting and cleaning a huge amount of data for a project. This project required a detailed strategy to guarantee flawless data which ensures great performance. I get a lot of satisfaction while hunting data for each project because of their dynamic nature, it continuously presents me with fresh problems and opportunities to put my creativity and problem-solving skills to use.”
Tip: Rehearse your script until you’ve memorised it. If you ace the first half of the interview, the remainder of the questions will flow more naturally for you.
2 – Why do you want to work for us?
Employers are very curious to hear about your commitment to their company. This is your opportunity to convince them of it:
“I’m mostly looking for opportunities with companies that value lifelong learning and acknowledge how quickly technology is developing. I selected (Name of Organisation you’re applying to) because working there allows me to continually improve my skills and learn new things while partnering with business leaders who motivate and empower me to move further in my data engineering career.“
Tip: To show that you have done your study, mention any projects you find inspiring or your compatibility with the business culture.
3 – Where do you see yourself in the next five years?
By asking you this question, your potential employer is attempting to figure out whether you view this position as a wise career move. A productive and highly retained employee is more likely to be fully engaged.
“At this stage of my career, I want to work for an organisation where I can collaborate with intelligent people. I’d like to have more authority over my career direction in the future.”
Tip: Even if you have a lot of ambition, avoid mentioning during the interview that you want to “take their job” or “run the company.”
4 – Why did you choose front-end development as a profession?
Employers ask this because they want to know who their employees will be working with. Your educational background, prior experiences, and motivations for wanting to work in this industry are all worthwhile discussion topics. You might perhaps elaborate on how your lifelong passion for creation inspired you to pursue this career path, or simply state that you want to create engaging, user-friendly web designs so that people’s lives are made easier.
Tip: Decide how much information you want to share with the interviewer and how casual or serious the environment of the company seems (you can usually figure this out by studying the area).
Performance-Based Questions
5 – What is Data Engineering?
Although this may seem like a fairly simple data engineer interview question, it might still be asked of you, depending on your degree of expertise. Your interviewer wants to know how you define data engineering specifically, which shows that you are aware of the nature of the profession.
This is how you can answer this question:
“Large data sets must be transformed, cleaned up, profiled, and aggregated, to put it briefly. You can go even further and go into the day-to-day responsibilities of a data engineer, such as creating and extracting random data queries, managing the data governance of an organisation, and so on.”
6 – What distinguishes an operational database from a data warehouse?
Although it might be considered an entry-level question in some roles, this data engineer interview question may be more appropriate for candidates with an intermediate skill set.
In your response, you should mention that:
“Databases that use Delete SQL commands, Insert, and Update are operational standards with a focus on quickness and effectiveness. As a result, data analysis may be a little more challenging. A data warehouse, on the other hand, places more emphasis on aggregations, calculations, and select statements. Because of these, data warehouses are a great option for data analysis.”
7 – Are you able to distinguish a Data Scientist from a Data Engineer?
The purpose of this question is to gauge how well you comprehend the various job functions that make up a data warehouse team. Both roles are different from one another, even if their duties and talents frequently overlap.
“Data scientists analyse and understand complicated data, whereas data engineers create, test, and manage the entire architecture for data generation. They frequently concentrate on organising and translating big data. Data engineers are needed to build the infrastructure that data scientists need to function.”
8 – Which Python libraries are most efficient for data processing?
This question can be asked to gauge your technical knowledge and expertise in the field. You should have a detailed knowledge of the technical aspects of your field. You can answer it something like this:
“Numpy and pandas are the two most widely used libraries for data processing. I use Dask, Pyspark, Datatable, and Rapids to process huge datasets in parallel. Each has advantages and disadvantages, and we must comprehend the application in light of the data requirements.”
9 – How do data modeling design schemas work?
Design schemas are crucial to data engineering, therefore make sure to be precise while presenting the ideas. You can say something like this:
“The simplest sort of data warehouse schema is the star schema, which contains a fact table with numerous related dimension tables, giving it the appearance of a star. The spokes of a snowflake are created by the inclusion of additional dimension tables to the star schema in the snowflake schema.”
10 – What is data orchestration, and what mechanisms can you apply to carry it out?
This is one of those questions that employers use to gauge your theoretical knowledge. Use precise answers to leave an impact and make your interview process smoother. You can say something like this:
“Data orchestration is an automated method for gaining access to unprocessed data from various sources, executing data cleaning, transformation, and modelling procedures, and delivering it for analytical activities. Apache Airflow, Prefect, Dagster, and AWS Glue are the most often used tools.”
11 – What are the advantages of using clusters in Kafka, and why do we do so?
This question will test your skill level and whether you are updated about the latest trends in the field. Show employers your enthusiasm and your will to learn and adapt to new trends in the field:
“Multiple brokers are used in the Kafka cluster to distribute data across numerous instances. Without any downtime, it may be scaled. To prevent delays, Apache Kafka clusters are deployed. Other Kafka clusters will be employed to provide the same services if the original cluster fails.
There are Topics, Broker, ZooKeeper, Producers, and Consumers in the Kafka cluster architecture. It manages huge data streams, which are needed to build data-driven applications.”
12 – Can you name a few of Hadoop’s important features?
Hadoop is an open-source architecture for data storage and application execution that offers enormous quantities of processing and storage capacity. You should mention that it is compatible with many types of hardware which makes it simple to access because your interviewer is trying to determine whether you grasp its significance in data engineering.
You can answer it like this:
“Data processing is supported quickly by Hadoop, and it is stored in a cluster that is separate from the rest of its operations. With separate nodes (groups of computers networked together to compute several data sets simultaneously), you can make three clones of each block.”
13 – How would a data migration from one database to another be verified?
A data engineer’s top priorities should be to ensure the accuracy of the data and prevent data loss. The purpose of this question is to help hiring managers understand how you would validate data.
You ought to be able to discuss the proper validation types in various contexts.
For instance, you might propose that validation be accomplished through a straightforward comparison or after the entire data migration.
14 – Can you explain NameNode to me? What happens if NameNode stops working or crashes?
This type of question will test your problem-solving skills in case of an emergency, and how you handle those issues, apart from your technical expertise employers also want to know your behaviour towards the problem and whether you can handle the stress or not, you can answer it like this:
“It is the focal point or core of the Hadoop Distributed File System (HDFS), however, it doesn’t hold any data. Metadata is kept there. For instance, which rack and DataNode the data is stored on when it is stored in DataNodes? It keeps track of the many files found in clusters. There is typically only one NameNode, thus if it fails, the system might not function.”
15 – What happens if an invalid data block is found by Block Scanner?
One of the most frequent and well-liked interview questions for data engineers is this one. In your response, you should list all the actions a block scanner does when it discovers a faulty block of data.
Try answering it like this:
“First, DataNode informs NameNode of the faulty block.NameNode creates a replica from a model that already exists. The NameNode makes replicas by the replication factor if the system does not destroy the faulty data block.”
