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Looking for a new opportunity in a Data Engineering role as a fresher or experienced can be challenging. Because of the competitive market,it is essential to prepare for the interview.

Hence, we can explore the top data engineer interview questions and answers to help you prepare. These questions aim to test your understanding of how these critical systems operate. Also, they test how you would respond to restraints and faults in their design and implementation. You will also find the gist of the type of answers the interviewers are looking for. 

What is Data Engineering?


Data engineering is the process of developing and constructing extensive systems for the gathering, storing, and analysis of data. It has applications in almost every industry because of its vast field. In short, data engineers collect and process raw data into information that data scientists and business analysts can understand.  Hence, companies use these collected data to leverage their business. Thus, the demand for skilled data engineers will always be there. 

Skills Required for a Data Engineer

The data engineer interview questions are based on the following skills: 

1. Programming

Strong programming knowledge with expertise in specific programming languages, such as Python, Scala, Java, etc., is required. Additionally, these programming languages support data pipeline development, data transformation implementation, and workflow automation.  

2. Databases

It is essential to have an in-depth knowledge of relational and NoSQL databases. Therefore, the key to success is to use a correct database for specific use cases and design efficient data schemes. 

3. Big Data

Familiar with big data technologies is a must. It,therefore, helps efficiently analyze large datasets. Thus, the technologies are Hadooba, Hive, and Spark.

4. ETL Tools

Data engineers must know how to design and manage ETL tools. Some of the ETL tools are Apache Nifi and Talend. 

5. Cloud computing services

Data Engineers must be experts in cloud platforms like Google Cloud, AWS, or Microsoft Azure. Consequently, they have a significant role in modern technology.

6. Data Warehousing and Architecture

A good grasp of building and working with data warehousing is essential. Also, the knowledge to build complex business database systems is crucial. 

Data Engineer’s Responsibilities and Roles

Some data engineer interview questions are based on the roles and responsibilities. The following are the roles and responsibilities of a data engineer: 

1. Data Collection and Integration

Data engineers collect data from various sources,  such as databases, APIs, external providers, and streaming sources. In addition, they use these data to design and implement efficient data pipelines to ensure a smooth flow of information into the data warehouse or storage system.

2. Data Storage and Management

Besides data collection, the data engineers are accountable for the proper storage and management. It also involves appropriate database systems, optimizing data schemas, and ensuring data quality and integrity. Moreover, we must consider the scalability and performance while handling large volumes of data. 

3. ETL Processes

ETL is a fundamental process in which we design an ETL pipeline to transform raw data into a structure suitable for further investigation. 

Basic Data Engineer Interview Questions For Freshers

Let us explore some of the interview questions for freshers.

1. Define Data Modeling.

Data modeling is the process of breaking complex software designs into simple diagrams that are easy to understand. Also, it provides numerous advantages as there is a simple visual representation between the data objects and the associated rules.

2. What are some of the design schemas used when performing Data Modeling?

Two schemas used while data modeling are:

  • Star schema
  • Snowflake schema

3. What is Hadoop? Explain briefly.

An open-source platform for manipulating and storing data in Hadoop. Moreover, it is used for running applications in clusters. The primary benefit is the vast volumes of space required for data storage and a tremendous amount of processing power to manage an infinite number of jobs and tasks simultaneously. The three different modes of Hadoop are:

  • Standalone mode
  • Pseudo distributed mode
  • Fully distributed mode.

4. What is the distinction between organized and unorganized data?

Organized data consists of types such as text, numerals, and dates. Thus, they fit in data tables. Unorganized data do not fit in the data table because of their nature and size. e.g., videos, images, etc.

5. What are some of the essential components of Hadoop?

The main components while working with Hadoop are as follows:

  • Hadoop Common consists of all libraries and utilities commonly used by the Hadoop application.
  • >The Hadoop File System (HDFS) stores data when working with Hadoop. It provides a very high bandwidth distributed file system.
  • Hadoop TARN or Yet Another Resource Negotiator manages resources in the Hadoop system. YARN also helps in Task scheduling.
  • Hadoop MapReduce provides user access to large-scale data processing.

6. What is a NameNode in HDFS?

NameNode, a vital part of HDFS, stores the HDFS data and keeps track of the files in all clusters. However, we store the data in the DataNodes instead of NameNodes.

7. What is Hadoop Streaming?

Hadoop streaming is a utility provided by Hadoop for creating maps and performing reduction operations. Later, we submit it to a specific cluster.

8. What are some of the essential features of Hadoop?

  • Hadoop is an open-source platform.
  • Hadoop works based on distributed computing.
  • It has faster data processing because of parallel computing.
  • We store data in separate clusters.
  • Priority is given to data redundancy in order to ensure no data loss.

9. What is meant by Block and Block Scanner in HDFS?

Block, the minor factor, is the single entity of data. Basically, Hadoop breaks a large file into smaller chunks called blocks. Moreover, we use a block scanner to confirm whether the loss of blocks created via Hadoop is effectively placed on the DataNode.

10. How does a Block Scanner handle corrupted files?

  • When the block scanner has a corrupted file, the DataNode informs this file to the NameNode.
  • The NameNode creates replicas of the original (corrupted) file.
  • If the replicas and the replication block can match, then they do not remove the corrupted data block.

11. How does the NameNode and the DataNode communicate with each other?

The NameNode and the DataNode communicate through messages. We send the following two messages across the channel:

  • Block reports
  • Heartbeats

12. What is meant by COSHH?

Classification and Optimization-based Scheduling for Heterogeneous Hadoop systems or COSHH provides scheduling at both the cluster and the application levels. Thus, it has a positive impact on the completion time for jobs.

13. Briefly explain Star Schema. 

Star schema, or the star join schema, is the most straightforward schema in Data Warehousing. Its structure is similar to a star. It also consists of fact tables and associated dimension tables. Hence, Big data uses the star schema.

14. Explain Snowflake in brief.

The snowflake schema is an extension of the star schema with more dimensions. The shape suggests its name. Following normalization, the data is structured and split into more tables. 

15. Mention the differences between Snowflake Schema and Star Schema.

Star schema uses denormalization and redundancy. Thus, it improves read performance but can lead to broader dimension tables that consume more storage. Snowflake schema provides a bottom-up approach that uses normalized data. It also makes it easier for users to drill down for data and compare data points.

16. What is Big Data?


Big Data means vast data. Besides, we must handle it in a variety of ways. But it helps find crucial trends and patterns in people’s behavior and interactions.

17. Why do Data Engineers need SQL?

Data Engineers use SQL to interact with databases. Moreover, it helps them exchange and analyze data.

18. Explain how Cloud Computing helps with Data Engineering.

Cloud Computing gives on-demand resources, making handling, analyzing, and storing considerable data easier. Additionally, it allows Data Engineers to work with big data more efficiently and cheaply.

19. What does Data Profiling mean?

Data Profiling indicates analyzing and gathering information about data. Thus, it helps analyze the data quality and further data processing, like cleaning or transforming. 

Intermediate Data Engineer Interview Questions

20. What is the meaning of FSCK?

FSCK, or File System Check, is one of the necessary commands used in HDFS. Thus, we use it primarily for checking problems and discrepancies in files.

21. What is ETL and its importance in data engineering?

The expansion of ETL is Extract, Transform, and Load. We acquire data from various sources, convert it to a suitable format, and loaded into a data warehouse or lake. ETL helps the organization collect, clean, and transform data into a structured format for further analysis. Furthermore, data will be in a raw, unstructured format without ETL. Thus, analyzing data that would remain in its raw, often unstructured state is complex, making exploring and gaining insights challenging.

22. Describe how a Data Lake differs from Data Warehousing. 

It is usually stored, and schema consistency is enforced by it. This is not the case; it can hold all types of data, including structured and unstructured. Also, it works well for exploration and massive data because it doesn’t have a predetermined format for storing data. Data analytics are a good fit for data warehouses. However, data lakes are great places to store and investigate information.

23. What are primary and foreign keys, and how are they used in database design?

In a database table, every row has its distinctive attribute, the primary key.  Thus, it ensures that each row is distinct and allows users to access and reference individual records. A foreign key, on the other hand, helps two tables to establish a link with each other. It also creates a connection between the tables, allowing referential integrity. Thus, foreign keys aid data consistency and enforce relationships among related data.

24. What is the CAP theorem, and how does it relate to distributed systems in data engineering?

Achieving consistency, availability, and partition tolerance simultaneously in a distributed system is impossible; this is a CAP or Brewer’s theorem. This theorem is important in distributed systems because it allows for design trade-offs. For example, in the face of network partitions (P), you may have to choose between solid data consistency (C) and high availability (A).

25. What is the purpose of partitioning in distributed data processing frameworks like Hadoop or Spark?

Partitioning breaks a large dataset into smaller ones. This manageable subset is called a partition. Thus, it aids in parallelizing data processing jobs across numerous nodes in a cluster. Also, distributed systems like Spark and Hadoop process data by splitting data into partitions. It helps them manage data efficiently, as each node can work on its partition concurrently.

26. Explain data serialization and its significance in data engineering.

Data serialization is the way to go when dealing with complicated data structures or objects. This makes storing, transferring, or reconstructing these formats very easy. Because it facilitates data consolidation into a single, easily-processable format, it represents a major advancement in data engineering. Parquet, JSON, and Avro are the most common serialization formats.

27. How do you ensure data quality in a data pipeline, and what are some common issues to monitor?

In a data pipeline, we can ensure data quality through various methods. They are data validation, cleansing, and monitoring. Common data quality issues include lost values, identical records, irregular formatting, and incorrect data. Data quality monitoring and data validation rules can be used to find and fix these problems. This way, you can ensure the data is accurate and dependable through the pipeline.

28. What is the difference between batch and stream processing? Provide use cases for each.

Processing of data in large, distinct fragments is batch processing. In contrast, processing real-time data, one record at a time, is stream processing. When delay in processing is not a significant concern, we go for batch processing. e.g., generating daily reports or historical data analysis. When we cannot afford delay in processing, we use stream processing. e.g., real-time analytics, monitoring, and fraud detection.

29. Can you explain the concept of data lineage and why it is crucial in data engineering and compliance?

Data lineage tracks data as it traverses through different data pipeline stages. Moreover, it helps the engineer understand the data’s origin, transformation, and consumption. Data lineage is crucial for compliance. Therefore, it ensures data governance and regulatory requirements are met by providing a clear audit trail for data. Data lineage also aids with debugging and optimizing data pipelines.

30. What is Data Transformation?

Data Transformation refers to converting data between formats. It also ensures that we can place all data from various sources together for analysis.

Advance Data Engineer Interview Questions

31. What are some of the methods of Reducer?

The three main methods of Reducer are:

  • setup(): Used to configure input data parameters and cache protocols.
  • cleanup(): Removes the temporary files stored.
  • reduce(): The method is called once for every key, and it is the most critical aspect of the reducer.

32. How is data security ensured in Hadoop?

We can handle the data security in Hadoop in the following ways:

  • Firstly, secure the authentic channel connecting clients to the server.
  • Secondly, the clients use the stamp they received to request a service ticket.
  • Lastly, the clients use the service ticket to connect to the corresponding server authentically.

33. How does Big Data Analytics help increase a company’s revenue?

Big Data Analytics helps increase the company’s revenue in the following ways:

  • Effective use of data to correlate to the structured growth
  • Effective customer value growth and retention analysis
  • Workforce forecasting and improved staffing strategies
  • Reducing the production cost majorly

34. What is the difference between a Data Architect and a Data Engineer?

A Data Architect is responsible for handling data from various sources. Data handling skills are necessary for a data architect. The Data Architect is also concerned about the conflicts in the organization model because of data changes.  On the other hand, a data engineer is primarily responsible for helping the data architect set up and establish the data warehousing pipeline and the architecture of enterprise data hubs.

35. What is the data stored in the NameNode?

The nameNode mainly consists of all the metadata details for HDFS, such as the namespace attributes and the personal block details.

36. What is meant by Rack Awareness?

Rack awareness is an idea in which the NameNode uses the DataNode to boost the incoming network traffic while concurrently executing reading or writing operations on the file, which is the most immediate to the rack from which we call the request. 

37. What is a Heartbeat message?

A heartbeat message is how the DataNode interacts with the NameNode. It is a vital signal the DataNode sends to the NameNode in a structured interval to indicate that it’s operational.

38. What is the use of a Context Object in Hadoop?

A context object and the mapper class communicate with the other parts of the system. System configuration details and jobs in the constructor use the context object.  It also sends information to functions like setup(), cleanup(), and map().

39. What is the use of Hive in the Hadoop ecosystem?

Hive provides the user interface to handle all the stored data in Hadoop. Besides, The data is mapped with HBase tables and used as required. Hive queries (similar to SQL queries) are performed to be altered into MapReduce jobs. It keeps the complexity under check when executing multiple jobs simultaneously.

40. What is the use of Metastore in Hive?

Metastore is a place for storing the schema and Hive tables. We store data such as definitions, mappings, and metadata in the Metastore. Later, it is stored in an RDMS when required. 

41. Explain the concept of Data Sharding and how it affects database scalability.

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Data Sharding involves splitting an extensive database into smaller, more manageable pieces, or ‘shards,’ distributed across multiple servers. It also enhances scalability, allowing the database to handle more requests by spreading the load.

42. How would you design a system to deduplicate real-time streaming data?

Designing a system to deduplicate streaming data involves using techniques like Bloom Filters or Cuckoo Filters to inspect for duplicates efficiently, windowing, and time-based checks to ensure data consistency.

43. In data processing frameworks such as Apache Spark, explain the application of Directed Acyclic Graphs (DAGs).  

In frameworks like Apache Spark, DAGs portray a series of analyses conducted on data.  Besides, each node denotes a procedure, and the edges depict the data flow. DAGs permit fault tolerance and optimization as they undoubtedly describe stages of analysis.

44. How can eventual consistency be handled in a distributed database system?

We can address the eventual consistency by enforcing tools like Conflict Resolution Strategies (e.g., Last Write Wins), Version Vectors, or Quorum-based Replication to ensure that, over time, all duplicates combine to the same state.

45. Explain how a Bloom Filter works and its usage in a data engineering pipeline.

A Bloom Filter is a probabilistic data structure used to experiment whether an element is a set member. It can present false positives but not false negatives. It also reduces unnecessary disk I/O or network calls, like checking if a key exists in a database.

46. How would you implement data retention policies in a data warehouse?

Enforcing data retention involves placing Time-To-Live (TTL) guidelines, archiving techniques, and partitioning data based on time, permitting efficient deletion or archiving of old data.

47. Explain how a Time-series Database differs from a traditional Relational Database and provide examples.

Time-series Databases (e.g., InfluxDB, TimescaleDB) address time-stamped data and are adequate for write-heavy workloads. However, Relational Databases (e.g., MySQL, PostgreSQL) may need to perform more adequately as time-series data.

48. How would you ensure data quality and integrity while ingesting data from multiple heterogeneous sources?

Providing data quality involves executing data validation inspections, schema validation, de-duplication strategies, and data profiling. Thus, we can log abnormalities and inconsistencies and fixed using predefined rules or manual intervention.

49. Can you create multiple tables for an individual data file?

Yes, creating more than one table for a data file is possible. In Hive, we store schemas in the MetaStore. Therefore, obtaining the result for the corresponding data is very easy.>

50. Describe Indexing

Indexing improves database performance by minimizing the number of disc accesses required when running a query. It is also a data structure strategy used to quickly find and access data in a database.

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In short, Data Engineering is a demanding career requiring much effort to become one. As a data engineer, you must prepare for data science challenges that may arise during an interview. Above all, many problems have multi-step solutions, and planning them allows you to map solutions through the interview process.  Here, you will get information about commonly asked interview questions on data engineering. You will also ace the interview with your responses.

Recommended Reads

  1. How Data Engineering Is Different From Data Science?
  2. Top 5 Data Science Projects for Beginners in 2024
  3. Top 10 Important Data Modeling Tools to Know in 2024
  4. Scope of Data Science in India: Career, Eligibility, Jobs (2024)
  5. How To Learn Data Science In 2024

Frequently Asked Questions

1. How do I prepare for a data engineering interview?

Ans. Talk through your process. It is the most crucial step. More than knowing how to write code and assemble data is required. Also, you must be able to communicate your process and decision-making to the interviewers. Therefore, practice by talking through a recent project to a friend unfamiliar with big data.

2. How do you crack data engineer interview questions?

Ans. To prepare for the Data Engineer Interview:

  • Master the SQL: You should practice creating, modifying, and managing databases.
  • Solve coding challenges: Solve Python, Scala, or C++ coding challenges.
  • Design an ETL pipeline: Create data, ETL, or delivery pipelines.

3. Is DSA required for data engineering?

Ans. Data structures and algorithms, or DSA, are vital for data engineers. Therefore, Data engineers must understand different DSAs to design and implement efficient and scalable data processing systems.

4. Is data engineering a tough job?

Ans. The job of a data engineer is both challenging and rewarding. But it needs effort to have a promising career as a data engineer. Thus, Data engineering is a broad term filled with tools, buzzwords, and ambiguous roles.

5. What are the primary skills of a data engineer?

Ans. The primary skills a data engineer must acquire and develop are listed below: 

  • Coding skills.
  • Database system expertise.
  • Understanding of data warehouse
  • Knowledge of the distributed systems.
  • Understanding of the application programming interface (API).
  • Critical thinking skills. 

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