30 Best Data Engineer Interview Questions And Answers In 2023

Interviews are steps in landing a job that most applicants are nervous about. It doesn’t matter the field you’re going into, whether it’s an entry-level, intermediate, or advanced; candidates are always scared because they don’t know what to expect.

However, one thing about job interview questions is that they’re always set to test your knowledge, competency, and expertise in your field of interest in data engineering is not different.

Data engineering jobs are getting more competitive and the best way to prepare is by revising likely questions you’ll encounter at the interview.

To help you get ready for the interview, here are some of the best data engineering interview questions you’ll likely encounter and answers to answers your assessors would want to hear.

30 Best Data Engineer Interview Questions And Answers

1. What Is Data Engineering?

This question might look like a simple question that might not come up in your interview, but is one of the questions that can be asked in your interview.

 Data engineering deals with the application of data collection and research. It is one of the terms used in big data.

Data Engineering looks at transforming, cleansing, profiling, and aggregating big data sets. It aims at converting raw data into useful information.

The duties of a Data Engineer include owning a company’s data stewardship and ad hoc data query building and extraction.

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2. Why do you want a career in data engineering?

This is another likely question you might encounter at an interview. Here you can start by telling the interviewer your interest in pursuing a career in data engineering, your passion for the field, and what motivates you.

Most organizations prefer to employ individuals who’re passionate about their career choice. You can also share your experiences and projects you’ve worked on here.

3. What Is Data Modelling?

Data modeling is the process of recording and documenting complex software design as a diagram by using certain formal techniques for easy assimilation by anyone.

It is a method of documenting a simplified diagram of a software design and the data elements in it applying symbols and text to represent the data and its flow.

Data models serve as blueprints for creating a new database.

4. What are the various types of schemas in Data Modelling?

When faced with this type of question at an interview, remember that there are mainly two types of data schemas in Data modelling: star and snowflake.

5. What important skills should a Data Engineer have?

To be a successful and competent professional Data Engineer, you’ll need to possess skills like:

  • Adequate knowledge of data modelling.
  • In-depth knowledge of database architecture and database design, including SQL and NoSQL.
  • Data visualization.
  • Great computing and math skills.
  • Real work experience with distributed systems like Hadoop (HDFS) and data stores.
  • Communication skills, analytical skills, and critical thinking skills are also handy.

You can highlight each of these with scenarios where they are applicable.

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6. Explain the Components of A Hadoop application

The components of a Hadoop application are:

Hadoop MapReduce

This software framework aids in writing applications that process large amounts of data.

Hadoop Common

This is a basic set of tools and libraries used by Hadoop.

Hadoop YARN

This aids resource management within the Hadoop cluster. It can also be utilized for task scheduling for users.


HDFS is an acronym for Hadoop Distribution File System. And it is the main data storage system used by Hadoop software.

7. What is NameNode?

NameNode is the core part of HDFS. It saves data from HDFS and tracks different files across the cluster. The data is stored in DataNodes.

8. What are the characteristics of Hadoop, and list the various XML configuration files in Hadoop?

The characteristics of Hadoop are:

  • Hadoop aids quicker distributed processing of data.
  • Stores data in a cluster, separating it from the rest of the operations.
  • It is an open-ware framework.
  • It lets you create 3 replicas for each block with different nodes.
  • Hadoop is also compatible with lots of different hardware. Accessing new hardware within a particular node is also easy. 

Hadoop comprises five XML configuration files comprises and they are:

  • Core site
  • Yarn site
  • HDFS site
  • Mapred site

YARN means Yet Another Resource Negotiator.

9. What is the difference between a Data warehouse and an Operational Database?

People applying for entry-level and intermediate-level jobs as data engineers could come across this question.

To answer this question you’ll start by stating that databases that use Insert, Update and Delete SQL statements are standard operational database that focuses more on efficiency and speed, this makes data analysis a bit complex.

However, the main focus of database warehouses is the calculation, aggregation, and select statements, making database warehouses the best choice for data analysis.

10. What is the meaning of ARGS and Kwargs?

This data engineering question at interviews focuses on your knowledge of complex coding skills. Here you answer by saying that ARGS defines an ordered function while Kwargs represent unordered arguments used in a function.

You can also write this code down to show your professional coding skills.

11. List the essential frameworks and applications for data engineers

Here your assessor will test you to know if you’re eligible and have what is required to handle the job. You can start by correctly listing the frameworks that match your level of expertise.

You can list applications SQL, Hadoop, Python, CSS, and others including your experience with each. 

12. Explain the primary methods of Reducers


Used for configuring parameters like the size of input data and distributed cache.


Used for cleaning temporary files.


This is the main framework of the reducer and it is deployed once per key with the associated reduced task. 

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13. What is Star Schema

Star schema or star join schema is the simplest form of Data warehouse schema. It is called a star schema because of its star-like structure.

The star’s core could have one fact table and multiple associated dimension tables in a star schema. It is used to query large data sets.

14. What are the four Vs of big data?

The four Vs of big data are

  • Volume
  • Velocity
  • Variety
  • Veracity

15. What is the difference between a Data Engineer and Data Scientist?

Here the assessor is trying to test your knowledge of the different job roles in a Data warehouse. Though both of them have similarities there is still some noticeable difference.

A Data engineer develops, test, and maintain the complete architecture for data generation while a Data scientist analyzes and interpret complex data.

They both focus on the organization and translation of big data but data scientists need the data engineers to build the structure for them to work with. 

16. What are the responsibilities of a Data Engineer?

Since organizations wouldn’t want to waste their time and resources employing inadequate candidates the assessors would love to know if you understand the responsibilities of a Data Engineer. You can state important tasks performed by data engineers like:

  • The development, testing, and maintenance of architectures.
  • Data acquisition and development of data set processes.
  • Make sure the design is in line with the organization’s goals.
  • Creating pipelines for various ETL operations and data transformation.
  • Finding ways to improve data reliability, quality, accuracy, and flexibility.
  • Developing machine learning and statistical models.
  • Simplifying data cleansing and improving the re-duplicate and building of data.

17. What are the steps in deploying a big data solution?

The following are the steps in deploying big data solution:

  • Integrate data using sources like MySQL, SAP, Salesforce, and RDBMS.
  • Store the data extracted in one of this two a NoSQL or HDFS.
  • Deploy big data solutions by using processing frameworks like Spark, Pig, and MapReduce.

18. What is the Snowflake schema?

A snowflake schema extends a star schema and puts additional dimensions. It got its name from its snowflake-looking diagram. The dimension tables are normalized that splits data into additional tables. 

19. What is the difference between Star schema and Snowflake schema?

Before differentiating between them it is important to know that design schemas in Data modelling are fundamentals of data engineering. The two design schemas in Data engineering are star schema and snowflake schema.

 The difference between them is that the star schema dimensions hierarchy is stored in dimensional tables, has high chances of redundancy, is a basic database design, and offers a quicker way for cube processing.

While in the snowflake schema each hierarchy is stored in different tables, there’s a reduced chance of redundancy, the database design is complex, making the cube processing slow.

20. How would you validate a data migration from one database to another?

One of your main goals as a data engineer is data validity and ensuring that none is lost. Most assessors would like to know what your answer would be concerning this.

As a data engineer, you should be able to talk about appropriate validation types in various circumstances.

You can also say that validation can be a simple comparison or it can come after the complete data migration.

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21. Have you ever encountered a problem in one of your projects and how did you successfully handle it?

Organizations are interested in knowing how their potential employees will act in different scenarios and how they manage or overcome challenges.

To accurately answer this question using the STAR method which involves:


Define the problem and the scenario that led to it.


Accurately describes how you overcame that problem. Tell them the responsibilities you took on to ensure that the problem was successfully handled.


Here you can elaborate more on the steps you took in solving that challenges.


Every action must have a result either negative or positive. Explain the results gotten from your action. The additional experience you gained, insights you got, and errors you noticed that would be avoided next time.

22. Have you transformed unstructured data into structured data?

This question is tricky because your assessor wants to know if you understand both data types and have practical experience. You can start by stating the difference between unstructured data and structured data.

For adequate data analysis, unstructured data must be transformed into structured data, explain the methods used in achieving this, you can as well use practical examples. 

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23. What steps to achieve security in Hadoop?

The steps necessary to achieve security in Hadoop include:

  • Securing the authentication channel of the client to the server. Provide time-stamped to the client.
  • The second step requires the client to use the received timestamp to ask for TGS for a service ticket.
  • Finally, the client uses the service ticket to self-authenticate a specific server.

24. List the various modes in Hadoop

The various modes in Hadoop are:

  • Standalone node
  • Pseudo distributed node
  • Fully distributed node

25. What is Big Data?

Big data comprises much unstructured and structured data that traditional data storage methods cannot process.

Hadoop is mostly used by data engineers to manage big data.

26. Have you experienced using the Hadoop framework to build data systems?

If you’re skilled in using Hadoop and have used it for a project before, you can give a detailed description of your work, focusing more on your skills using Hadoop.

You can mention that you used Hadoop because it is scalable and can improve data processing speed while maintaining its quality.

Some characteristics of Hadoop are:

  • It is Java-based and simple to use.
  • Since data is stored on Hadoop, it is easily accessible in case of hardware malfunction from other paths making it the best choice for handling big data.
  • Data is stored in a cluster, making it separate from other operations.

For entry-level graduates with little or no experience, you can learn more about the tool’s properties and characteristics.

27. What are the signals that NameNode receives from DataNodes?

NameNodes get information about data from DataNodes in the form of signals.

The two messages gotten are:

  • Block report signal is the list of data blocks stored on DataNodes and its functioning.
  • Heartbeat signal that the DataNode is alive and functional. It is periodic documentation to decide whether to use NameNodes or not. If no message is received, then that means DataNode is no longer working.

28. Explain the use of Reducer in Hadoop and the core methods of Reducers.

The reducer is the second step of data processing in the Hadoop framework. It processes the data output of the mapper and brings out the final result stored in HDFS.

The phrases in a reducer are:


The data output from the mappers is shuffled and used as the input for the reducer.


This is done alongside the shuffling and outputs from different mappers are organized or sorted.


Here the key-value pair is consolidated, providing the required output, which is finally stored in HDFS.

29. List the important fields or languages used by data engineers

Some languages or fields used by data engineers are :

  • Probability and linear algebra
  • Trend analysis and regression
  • Machine Learning
  • Hive QL and SQL database

30. Why does Hadoop use Context Object?

Hadoop framework combines the context object with the mapper to aid interaction with the other remaining system.

It collects information about the system configuration and job in its constructor.

Context allows the ease of moving data in setup, cleanup, and map methods.

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Data engineering is a good career choice, but landing your first big data engineering job requires a lot of preparation, learning, and practice.

Interviewers would look to know how knowledgeable you are and if you’re competent enough to handle a data engineering job since no employer wants to hire someone who’s below the required standard.

Don’t forget you’ll be applying for the job along with other competent candidates. that is why we have a list of questions you’ll likely encounter at a data engineering interview. 

Good luck.


  • Top 34 Data Engineer Interview Questions And Answers – Simplilearn.com 
  • Top 62 Data Engineer Interview Questions And Answers In 2023 – Guru99.com 
  • How Much Do Big Data Engineers Earn? Your Salary Guide For 2023 – Careerfoundry.com 


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