Data Engineering Interview Questions (Top 50 Questions)

Data Engineering is among the highest-paying jobs of 2022. There are a lot of people applying but only a few make it past the interview phase. Preparation is always better and it can boost your chance of being hired as a data engineer.

Therefore, we have prepared an accurate list of interview questions for you. The list consists of questions and answers for both, experienced data engineers and freshers.

To make things easier for you, we will divide this article into three sections:

  1. Basic Interview Questions for Data Engineers (10 Questions)
  2. Intermediate Interview Questions for Data Engineers (25 Questions)
  3. Advanced Interview Questions for Data Engineers (15 Questions)

Top 10 Most Asked Data Engineering Interview Questions (Basic) 

Data Engineering interview questions are of two types: basic and technical. One section evaluates your personality and compatibility with the workplace and the other evaluates your knowledge and skills in data engineering. As long as you know the right answers, you will certainly succeed in your data engineering interview.

  1. What is Data Engineering?

This may appear as a simple question but it often pops up during an interview. The interviewer already knows the standard definition. What they want to know is, what is your definition of Data Engineering? Make the answer simple – Data Engineering is transforming, cleansing, profiling, and aggregating large data sets. If you are feeling confident, discuss things further such as the responsibilities of a data engineer.

  1. Why Did You Choose Data Engineering?

What’s important to a company is to understand your motivations and interest behind choosing data engineering. Data Engineering is critical work, and unless you can exhibit a passion for the field, it is going to be tough to succeed in the interview. Start by sharing your story and insights, and make sure you highlight things that excited you the most about data engineering.

  1. Why Are You Interested in This Job?

Regardless of where you apply, this is one of those questions that always pop up. Your answer needs to satisfy the interviewer that you have done your homework and are the best fit. Identify several features of the job that excite you, and why you love this company.

  1. Why Should We Hire You?

This is where it gets tricky. You need to stay fully alert while answering this question since it can shift the interviewer’s mood drastically. Be concise and talk about your education, qualifications, skills, personality, and professional experience. Take a step further while talking about your personality and how you are the best fit for the organization’s culture.

  1. What Challenges Have You Faced During a Recent Project? How Did You Overcome Them?

Your employer wants to evaluate how you react to difficult situations. Your job is to ensure that you can successfully handle any challenges thrown your way. Use the STAR method to answer this question.

  • Situation: Give a concise description of the circumstances that caused the problem.
  • Task: Explain this bit in detail. This will let the interviewer know how well you perform a certain role.
  • Action: Share the steps you took to resolve the issue.
  • Result: Highlight the consequences of your actions. In addition, be sure to share what you learned as well as the stakeholders.
  1. How Do You Handle Job-Related Crisis as a Data Engineer?

Chances are you’ll either be asked the question above or this one. It’s rare for the interviewer to ask both. Data Engineers have a lot on their shoulders and it is normal to come across challenges on the job now and then. Be completely honest. Create a hypothetical situation for the interviewer, and tell them how you will deal with it. In addition, how you could have prevented it. 

  1. Have You Earned Any Certifications in Data Engineering?

Most interviewers ask this question because they are handpicking data engineers that are serious about advancing their careers. Certifications are strong evidence that you have put in a serious effort to develop new skills, master them, and implemented them to the best of your ability. Having a Google, AWS, or Microsoft Azure certification as a data engineer will boost your chances of getting hired.

(If you are serious about getting a certificate then take advantage of Skillcurb courses to easily get your certification as a: Google Certified Professional Data Engineer and Google Cloud Certified Associate Cloud Engineer.)

  1. Do You Have Prior Experience Working in The Same Industry?

This question may shake your confidence if you don’t have prior experience. However, the question is aimed to understand what sort of exposure you had and whether or not the work was the same as the one you are applying for. Give an honest answer and elaborate on your experience as well as the tools and techniques you have used.

  1. What is Your Plan After Joining us as a Data Engineer?

This is an important data engineering interview question. Keep your explanation concise. Tell the interviewer how you will bring about a plan that benefits that company and how would you implement it. Be sure to mention that the first step is to understand the data infrastructure setup of the company. This plan could be anything, it could be things you are aiming to learn at the company to advance your skills and provide more efficiency.

  1. Do You Have Any Experience Working with Data Modelling?

This question is for an intermediate-level role. Start your answer with a yes or no. It’s completely fine if you have no experience working with data modeling. However, be sure that you explain everything you know about data modeling in a structured manner. This will give you an advantage by helping the interviewer understand that you have the potential and the necessary knowledge.

Top 25 Most Asked Data Engineering Interview Questions (Intermediate)

Unlike the previous section, there is only one right way to answer these data engineering interview questions – keep your answer concise and give an accurate answer. These test your knowledge and skills.

  1. What is Data Modeling?

Simple answer – Data Modeling involves documentation of complex software design in the form of a diagram. This makes it easy to understand for everyone. In other words, it is a conceptual representation of data objects. These data objects are associated with rules and different data objects.

  1. What is the Full Form of HDFS?

Just give a short answer – HDFS is short for “Hadoop Distributed File System”. Keep it brief unless the interviewer asks you to explain what is HDFS rather than asking for the full form.

  1. What is Hadoop?

Simply put, Hadoop is an open-source framework. The fundamental use of Hadoop is to manipulate and store data. In addition, it is also used for running applicant units, normally called clusters. When it comes to dealing with Big Data, Hadoop has always been the gold standard. It’s mainly because there is an advantage of the easy provision of a large quantity of space needed to store data and high processing power to deal with limitless tasks and jobs concurrently.

  1. What is Hadoop Streaming?

Hadoop streaming is a widely used utility that allows Data Engineers to create maps with ease. It also performs reduction operations and submits them to designated clusters.

  1. What are the Important Features of Hadoop?

The most important features of Hadoop are:

  • It is an open-source framework.
  • No data loss is ensured by giving priority to data redundancy.
  • Hadoop functions on basis of distributed computing,
  • Offers faster data processing thanks to parallel computing.
  • Data is safely stored away from operations in units called clusters.
  1. What is Block and Block Scanner in HDFS?

A Block is a singular entity of any data and it is the smallest factor. Every time Hadoop encounters a larger file, it automatically chops it down into smaller chunks called blocks.

A Block Scanner in HDFS simply verifies whether the loss-of-blocks by Hadoop has successfully made it to the DataNode or not.

  1. What is NameNode?

NameNode is one of the key components of HDFS. It’s a way of storing all the HDFS data while keeping track of files in all clusters simultaneously.

(Note: Data is stored in DataNodes, not NameNodes)

  1. What Steps Take Place When a Corrupted Data Block is Detected by a Block Scanner?

When the Block Scanner detects a corrupted data block:

  • The DataNode reports the corrupted file to NameNode
  • NameNode processes the corrupted file by creating replicas
  • If the replicas are created and the replication block shows a match, the corrupted data block is not removed.
  1. Name Two Messages from DataNode to NameNode.

Messages are means of communication between NameNode and DataNode. There are two messages:

  • Heartbeats
  • Block Reports
  1. What are Various XML Configuration Files in Hadoop?

The XML Configuration files available in Hadoop are:

  • Core-Site
  • HDFS-Site
  • YARN-Site
  • Maped-Site
  1. What are The Four V’s of Big Data?

The four V’s of Big Data are:

  • Velocity
  • Variety
  • Volume
  • Veracity
  1. What are The Main Methods of Reducer?

The main methods of reducer are:

  • Setup – used to configure parameters such as the size of input data and distributed cache.
  • Cleanup – used to clean temporary files.
  • Reduce – the heart of the reducer, it is called once per key with the associated reduced tasks.
  1. What Does COSHH Stand for?

COSHH is short for Classification and Optimization-based Schedule for Heterogeneous Hadoop Systems.

  1. What is Star Schema?

Star Schema is used for querying large data sets. It is also known as Star Join Schema and can be defined as the simplest type of Data Warehouse schema. The name comes from its star-shaped structure. The center of the star may contain one fact table and more than one associated dimension table.

  1. What is Snowflake Schema?

The snowflake Schema is an extension of the Star Schema. Its purpose is to add additional dimensions. The name comes from its snowflake-shaped diagram. In Snowflake schema, dimension tables are normalized which splits data into additional tables.

  1. How Do You Deploy a Big Data Solution?

There are three steps to deploying a big data solution:

  • Integration – extraction of data using data sources such as SAP, MySQL, RDBMS, etc.,
  • Data Storage – Extracted data stored in NoSQL or HDFS database.
  • Data Processing – This is the final step, where the solution is deployed using processing frameworks (Spar, MapReduce, etc.,)
  1. What is FSCK?

FSCK is short for File System Check. It is a command used by HDFS to check for inconsistencies and problems within a file.

  1. What happens if NameNode Crashes or Comes to an End?

NameNode is the centerpiece of HDFS, and it stores metadata, not the actual data. Normally, there is only one NameNode and if it crashes, the system becomes unavailable.

  1. How is Hadoop Related to Big Data?

This is a commonly asked question by interviewers. It’s a way to verify your data engineering knowledge and experience. Answer by explaining that Hadoop and Big Data are related as Hadoop is the most frequently used tool for Big Data processing. If you are familiar with the framework, it’s a plus!

  1. Describe the Different Components of Hadoop.

Hadoop consists of 4 components:

  • HDFS – stores all of the Hadoop data. It has a high bandwidth and seamlessly preserves data quality.
  • MapReduce – Used for processing large data volumes.
  • Hadoop Common – Group of functions and libraries that can be utilized in Hadoop.
  • Yarn (Yet Another Resource Negotiator) – used for allocation and management of Hadoop resources.
  1. How Do You Validate Data Migration from One Database to Another?

For all companies, the validity of data and ensuring no data is dropped is the top priority. As a data engineer, your responsibility is to ensure no data is dropped. Speak about appropriate validation types in different scenarios. Make suggestions that validation could be a simple comparison, or it can also take place after the migration completes.

  1. Do You Have Experience in Transforming Unstructured Data into Structured Data?

Most interviewers ask this question to evaluate your understanding of both data types and practical working experience. A good answer to this question would be to briefly distinguish between both categories. The unstructured data needs to be transformed into structured data to perform data analysis. Take a step further by discussing methods for transformation. In addition, share a real-world situation where you performed such a task. However, if you are a fresher then just discuss information relevant to academic projects.

  1. What are The Essential Frameworks and Applications for Data Engineers?

Understanding essential frameworks and applications is a crucial requirement for the position of a data engineer. Give a concise answer where you accurately mention the names of frameworks and applications along with your experience with each. List all the technical applications such as Hadoop, Python, MySQL, etc., If the mood is right, tell the interviewer about frameworks you would love to learn if given the opportunity.

  1. Do You Have Experience in Python, Bash, Java, or other Scripting Languages?

Scripting languages are important for data engineers as their work revolves around them. It is important you prepare ahead of time and learn the scripting languages and develop a level of proficiency in them to be able to perform the analytical tasks efficiently and automate the data flow.

  1. Do You Know the Difference Between a Data Engineer and a Data Scientist?

Most interviewers ask this question to assess your understanding of job roles within a data warehouse team. Data Scientists and Data Engineers work closely together and it is easy to confuse one for the other. However, they are quite different from each other.

Data engineers are responsible for developing, testing, and maintaining the entire architecture for data generation while data scientists have the role of analyzing and interpreting complex data. Data scientists need data engineers to create the infrastructure for them to work on.

Top 15 Most Asked Data Engineering Interview Questions (Advanced)

  1. What is the Use of *args and **kwargs?

*args and **kwargs are both functions. *args allows the user to define an ordered function for usage in the CLI (Command Line). On the other hand, **kwargs function helps denote a set of arguments that are in an unordered state and in line to be input to a function.

  1. How Do You See The Structure of a Database Using MySQL?

The correct way to see the structure of a database using MySQL is to use the “Describe” command. DESCRIBE Table name;

  1. Can You Search for a Specific String in a Column Present in a MySQL Table?

You can search for a specific string in the MySQL column using the regex operator.

  1. What is the Difference between Data Warehouse and Operational Database?

The question is intermediate-level but in some cases, it can also be considered entry-level. Data Warehousing primarily focuses on using aggregation functions, selecting subsets in data for processing, and performing calculations. On the other hand, the Operational Database focuses on speed and efficiency. The main focus here is using “delete” SQL statements, data manipulation, and more.

  1. Do You Have Experience Working with ETL? Which One Do You Prefer and Why?

The interviewer wants to know your understanding and experience with ETL (Extract Transform Load) tools and process. List the tools you are an expert with and pick a favorite one. Point out the key properties that make that tool stand out. Validating your preferences will demonstrate your knowledge and give the interviewer what he was looking for.

  1. What Collections are Supported By Hive?

Hive Supports complex data types such as

  • Map
  • Struct
  • Array
  • Union
  1. What Does “Skewed Tables” mean in Hive?

Skewed tables often contain column values. In Hive, a table can be specified as SKEWED during the creation step. The values of skewed are written into separate files while the remaining are sent to another file.

  1. What is SerDe in Hive?

Apart from writing your custom SerDe implementations, the following are some of the most popular implementations:

  • RegexSerDe
  • ByteStreamTypedSerDe
  • OpenCSVSerde
  • DelimitedJSONSerDe
  1. What is the Role of the .hiverc file in Hive?

.hiverc is the initialization file in Hive. It is initially loaded when you start the CLI for Hive. In addition, the initial values of parameters in the .hiverc file can be set as well.

  1. What Table Generating Functions are Available in Hive?

In Hive, the following are the table-generating functions:

  • JSON_tuple()
  • Stack()
  • Explode(array)
  • Explode(map)
  1. What Components are Available in Hive Data Model?

There are three components in Hive Data Model:

  • Tables
  • Partitions
  • Buckets
  1. Is it Possible to Create More Than a Single Table for Individual Data Files?

Yes, it is possible to create more than a single table schema for a data file. In Hive, the schema is saved in Hive Metastore. Based on the schema, dissimilar results from the same data can be retrieved.

  1. Did You Ever Work with Big Data Cloud Computing Environment?

The interviewer wants to know if you understand cloud computing. Since most companies are switching to Cloud Computing, knowledge of cloud computing is crucial. Answer the question by demonstrating that you are prepared for the possibility of working in a virtual workspace. In addition, share some advantages:

  • Access data in a secure way from anywhere.
  • Multiple backups in emergency scenarios.
  • Flexibility to scale the environment as needed.
  1. Can You Explain How Data Analytics and Big Data Increase Company Revenue?

Your interviewer wants to know that you understand the applications of Data Engineering. For the company, this is an important question as the main goal of every business is to boost its revenue. Therefore, companies prefer candidates who understand how they can help the company grow. Start by sharing these ways of increasing company revenue:

  • Increase customer value
  • Cut down production costs for the organization
  • Efficient data usage to ensure business growth
  • Improving staffing levels forecasts by turning analytical
  1. According to You, what are the Daily Responsibilities of a Data Engineer?

Finally, this question indicates that you understand the role you are applying for. Explain some essential tasks as a data engineer, such as

  • Developing, testing, and maintaining architectures.
  • Data acquisition.
  • Data set processes development.
  • Optimizing the design with business requirements.
  • Deployment of machine learning.
  • Deployment of statistical models.
  • Identification of ways to improve data accuracy, quality, flexibility, and reliability.
  • Development of pipelines for data transformation and various ETL operations.
  • Simplification of data cleansing.
  • Improvement of de-duplication and data building.


Data engineering interview questions can be of all sorts. This mostly depends on where you are applying and who is interviewing you. If you are applying somewhere at an entry level, you will only be asked the basic level questions and maybe a few intermediate level questions. However, if you are going for a big company, they’ll be asking you for everything between basic and advanced.

These interview questions are a result of numerous surveys to help you prepare for your data engineering interview. However, you must study and try to build your answers as well.