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20 Most Common Data Engineering Interview Questions

If you are interested in exploring a career in the data domain, there are three key avenues that you can explore: being a Data Scientist, a Data Analyst, or a Data Engineer.

Data scientists and Data Analysts focus on analyzing and working on the data to derive actionable insights. In contrast, data engineering is the process of developing a system that can collect large amounts of data, efficiently store it, and process datasets via analysis systems to use them for actionable reports or visualizations.

Since data engineering enables Data Scientists and Data Analysts to work efficiently, Data Engineers are in huge demand in multiple industries, be it a Silicon-valley corporate giant or a new-age startup. 

Data Engineers primarily design, build, test, and manage data that can then be used for data analysis, data visualization, business intelligence, research, and more.

Since data is now crucial for business success, data engineering is one of the fastest-growing career options, experiencing a 50% year-on-year growth in the number of open positions.

The average annual salary of a Data Engineer is $1,12,493, presenting an excellent career opportunity for those looking to upskill in their current role.

While technical expertise and knowledge of the latest software development trends are essential, it is also important to be prepared for Data Engineer interview questions.

Given the demand and competition in this space, your answers and knowledge will go a long way in getting selected for a data engineering role. To help you get an edge and ace the interview rounds, we have collated a list of the top 20 Data Engineer interview questions and answers you must be aware of.

So, if you have already started your Data Engineer interview preparation, here are the top questions to keep on your radar.


Basic Questions

We’ll first look at some of the basic Data Engineer interview questions that will be asked during the hiring process for freshers and senior-level profiles.

These questions help the hiring managers determine the candidate’s competency and gauge their Data Engineer interview preparation and knowledge. 

1. What is data engineering?

This may seem like a straightforward Data Engineer interview question, but the answer will determine how well you know the subject. The interviewer doesn’t want the textbook definition here but wants to know how well you know the subject and can communicate it. 

Here, your objective should be to simplify the concept and showcase your expertise in the domain.

Data engineering is the process of gathering information from numerous sources into a stable system. Raw data needs to be converted into structured data, i.e., extracting information in a format and model used by data scientists and analysts.

Thus, data engineering involves not just data collection and storage but also transformation, aggregation, cleansing, and profiling to help make it actionable. 

2. What is data modeling?

Getting deeper into the concepts, data modeling is the concept of extracting valuable information from raw data by creating a visual representation of the information.

Data is modeled according to the requirements of data scientists and analysts, which helps them identify relationships, find gaps, and derive insights from the data. This process is important to ensure that the data collected is used for business analysis and converted into useful information. 

3. How can you distinguish between structured and unstructured data?

A fundamental requirement for any Data Engineer is to know the difference between structured and unstructured data.

For these Data Engineer technical interview questions, you need to provide the interviewer with the key differences between structured and unstructured data, such as:

Unstructured Data Structured Data
Storage Type Stored in unmanaged file structures DBMS
File Formats STMP, XML, CSV, and others ADO.net, ODBC, and SQL
Integration Tool Manual data entry or batch processing ETL (extract, transform, load) tools
Analysis Complex to analyze and, in most cases, will need to be converted into a structured format Easy to analyze and interpret
Attributes Highly scalable and flexible since data is saved in its raw format Not flexible and scalable as it is dependent on the schema

4. Have you ever worked in a Big Data setup for the cloud? Would you recommend using a cloud-based environment?

If you are applying for a job in any new-age company, this Big Data engineer interview question will definitely be asked.

Since most companies are migrating their existing applications or storage to the cloud, there is a strong requirement for data engineers who understand the latest cloud computing capabilities. To answer this question, mention how Big Data in a cloud computing environment can efficiently enable the company to get many benefits like:

    • Secure access to information from any location (especially useful in a remote working setup)
    • Access controls ensure that despite working in a virtual workspace, every individual is only permitted to access particular information depending on their level of access
    • Flexibility to scale operations as needed
    • Backup facility to prevent any chances of data loss

5. Do you have experience in scripting languages like Python, Java, Bash, and others?

Coding expertise in scripting languages is a must-have for any data engineer, so you will be asked about your expertise in some key languages.

Since the follow-up will be Data Engineer technical interview questions, make sure you are honest when answering this question.

You should accurately mention the name of scripting languages like Python, Java, Bash, and others that you are familiar with, as well as your level of expertise with each. 

6. What is the difference between a Data Engineer and a data scientist?

It may seem like an easy answer, but most freshers are unaware of what differentiates data scientists from data engineers. So to help you answer this Data Engineer scenario-based interview question, here is what a data scientist and Data Engineer do:

A data scientist works on extracting value from a large or complex data set and will operate in multiple domains like business, government, and applied sciences.

Since data scientists focus on the outcome or research part of the data, their primary focus will be on data cleansing, analytics, visualization, and integrity, which allows them to derive insights relevant to their field.

Meanwhile, a Data Engineer is focused on developing and implementing data engineering technology to help data scientists and analysts derive actionable information from the data.

Data engineers work on collecting information from multiple sources, the efficient storage of this information, and the process of converting raw data into structured data, i.e., data curation, data optimization, data cleansing, data wrangling, and data warehousing. 

7. What, according to you, are the essential skills to be a data engineer?

Although there is no clear answer to this question since every organization needs data engineers to work on several aspects per their requirements, the objective here is to understand if you match the skill and expertise they are looking for.

Some of the must-have skills for data engineers that you should mention are:

  • Detailed understanding of data modeling
  • Knowledge of data warehousing tools like SQL and NoSQL
  • Data visualization and transformation knowledge
  • Experience with distributed systems like Hadoop, Spark, etc.
  • Knowledge of data warehousing and ETL tools
  • Ability to think out of the box and understand the requirement of the business team to convert raw data into a structured format
  • Robust mathematical, statistical, and computational skills
  • Programming knowledge in tools like Python, Java, Javascript, and others

8. What are the four Vs of Big Data?

The four Vs of Big Data define the characteristics of any Big Data environment. These are:

  • Volume
  • Velocity
  • Veracity
  • Variety

For managerial roles, the candidate should also mention that as an outcome of Big Data, the fifth ‘V,’ which is also crucial, is ‘Value.’ 

9. Why do you want to explore a career in Data Engineering?

The recruiter always wants to separate those trying to ride a wave from the ones who are serious about exploring a career in data engineering. This Data Engineer interview question is thus something you must expect, and the answer should be focused on your career goals and what you wish to achieve in data engineering.

While answering, ensure you have a firm understanding of data engineering, why it appeals to you, any background or previous experience that will help you excel in this field, and why you are the best person to implement data engineering for the organization.

Read the job description and research the company to help you answer this question successfully. 

10. Can you name the essential frameworks and applications for data engineers?

The objective of this question is to understand your overall expertise in the technical aspects of data engineering.

Some of the essential frameworks that data engineers should be aware of are SQL, Amazon Web Services, Hadoop, Python, Apache Kafka, Spark, and Snowflake.

In addition, some of the tools that are widely used in the industry include MongoDB, HBase, PostgreSQL, Amazon Redshift, Amazon Athena, and others. 

Advanced Technical Questions

Once the interviewer has asked the basic questions and is satisfied with your answers, they will move to the Data Engineer technical interview questions.

These need to be answered with precise technical details and showcase that you are the right fit for the role. 

11. What is Hadoop? What are the features of Hadoop?

Hadoop is an open-source and scalable software framework used for distributed storage and processing of large amounts of data. Some of the reasons why Hadoop is used in business implementations are its features like:

  • Scalability
  • Flexibility
  • Easy to use and implement
  • Data Reliability and security
  • High level of fault tolerance

12. Explain all the components of Hadoop.

The key components of Hadoop include:

  • Hadoop Common Library – contains the common set of commands and utilities for Hadoop
  • HDFS – essentially is a Hadoop Distributed File System that enables efficient storage
  • Hadoop MapReduce – implemented for large-scale data processing capability
  • Hadoop YARN – used for resource management within the Hadoop cluster

You will be required to mention these and explain where they are used. 

13. What is HDFS?

HDFS stands for Hadoop Distributed File System and handles large data sets running on particular hardware. HDFS acts as the primary data storage option and employs the NameNode and DataNode architecture to enable users to retrieve and store information in a scalable Hadoop cluster easily. 

14. What is COSHH?

COSHH stands for Classification and Optimization-based Scheduling for Heterogeneous Hadoop Systems and is a job scheduler that provides execution and multiplexing of multiple tasks in a typical data center. 

15. Can you explain indexing?

The objective of this question is to understand how well you know the fundamentals of data engineering and its usage. 

Indexing is a process that helps improve database performance and storage by reducing the number of disc access necessary to run a query. Indexing helps to structure data queries and link them with relevant fields in the table, helping establish a relationship.

16. Explain the XML configuration in Hadoop.

The XML configurations available in Hadoop are: 

  • Core-site
  • Mapped-site
  • Yarn-site
  • HDFS-site

17 Can you share details of the Snowflake Schema in Brief?

Design schemas are fundamental in data engineering, and you must accurately describe the Star schema and Snowflake schema.

The Snowflake schema is a logical arrangement of tables in multidimensional databases and is an enlarged version of the Star Schema. Snowflake schema helps organize tables and explain related dimensions as well as how they are interlinked with other dimensions, forming a snowflake pattern. 

18 What is the difference between Spark and MapReduce?

To answer this question, make sure you explain both Spark and MapReduce.

Spark is a MapReduce improvement in Hadoop and processes and retains data in memory for later use. MapReduce, on the other hand, processes data in the disc.

Due to this difference, Spark’s data processing speed is 100x faster than MapReduce, which is ideally used by companies with larger datasets. 

19. What are *args and **kwards used for?

A typical technical question, the *args function helps users to specify an ordered function in a command line, while the **kwargs function is used to express a group of unordered functions in a command line. 

20. Tell us about an algorithm used in your recent project. What made you select it?

One of the most common Data Engineer scenario-based interview questions, the objective here is to test how well you can implement the data engineering concepts in developing a product or upgrading a system. When answering this question, make sure to emphasize the critical aspects of your past project, like:

  • What was the objective of the project?
  • Why did you choose the particular algorithm?
  • What benefit or scalability does the algorithm offer?
  • What was the outcome? How did the algorithm help minimize effort?

The answer will help reflect your thought process and technical knowledge and help the hiring manager know whether you can simplify the existing process, which is crucial for data engineers. 

Having these Data Engineer interview questions and answers at your fingertips will help you get an edge over the competition and improve the chances of acing the interview.

Nonetheless, if you are also looking for an in-depth understanding of data engineering and to equip yourself with the skills to become job-ready, the Hero Vired Certificate Program in Data Engineering is the ideal way to get started and begin your journey to land your dream job.

Our 9-month program is self-paced and instructor-led, including online classes and video training to help you brush up on the fundamentals of data engineering and learn at your own pace.

The program includes the latest industry-acclaimed technology stack and will train you on data engineering and transformation techniques. 

It will help you learn technical aspects of data engineering like Python programming fundamentals, SQL and NoSQL databases, Scala programming, data transformation using Spark, and other software data engineering essentials.

This course will have you covered with not just the knowledge and skill-set to ace your interview but also a data engineering certificate that can greatly improve your chances of success.

So, start your preparation today and ace the Data Engineer technical interview questions easily!

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