Data Engineer Interview Questions- Check 40 Questions With Answers From Basic to Advanced Level 

Data engineering focuses on designing, building, and maintaining systems and frameworks to collect, store, and analyse large volumes of data. The demand for skilled data engineers is rising because most of the businesses rely on data for decision-making. To become a data engineer, you must know fundamental concepts and have advanced technical skills. In this article, we have given a comprehensive list of interview questions and answers. These questions and answers will help you understand the key areas of data engineering, such as data warehousing, ETL processes, and databases. Learning these questions will help you be well-prepared to show your skills and get a job as a data engineer.

Data Engineer Interview Questions with Answers 

Here is a list of 40 data engineer interview questions and answers about data engineering. Check them now to build your foundation in the concerned field.

Question No 1. What is a data pipeline?

Answer: A data pipeline is a process that automates the movement and transformation of data from various sources. It includes data collection, where raw data is stored from different sources such as databases, APIs, or files. Then, the data is transformed, which includes cleaning, enriching, and formatting the data into a usable form. Finally, the transformed data is loaded into a system like a data warehouse. Here, it is analysed and used for business insights. Data pipelines ensure efficient, consistent, and reliable data flow in the systems. They are essential for data processing and analytics.

Question No 2. What is the full form of ETL?

Answer: ETL stands for Extract, Transform, Load. “Extract” means recovering raw data from various sources like databases, files, or APIs. “Transform” refers to cleaning and converting the data into a suitable format. “Load” is the process of transferring the transformed data into a target system, such as a data warehouse or database. ETL processes help organisations consolidate data from multiple sources. This allows for comprehensive analysis and reporting.

Question No 3. What are the differences between a data warehouse and a database?

Answer: A database is designed to record and manage real-time data transactions. It supports CRUD operations (Create, Read, Update, Delete). A data warehouse, on the other hand, is optimised for analytical processing. It stores old data from multiple sources. Databases are used for day-to-day operations, while data warehouses are used for long-term data analysis. 

Question No 4. What is normalisation in databases?

Answer: Normalisation is a process in databases that reduces redundancy and improves data integrity. It involves organising data into tables and columns to ensure each part of the data is stored only once. The process also involves dividing larger tables into smaller ones. Normalisation helps eliminate duplicate data and anomalies during data operations, resulting in a more efficient and organised database structure.

Question No 5. What are the different types of joins in SQL?

Answer: In SQL, joins are used to combine rows from two or more tables based on related columns. The main types of joins are:

• Inner Join: Returns only the rows with matching values in both tables.
• Left Join (Left Outer Join): Returns all rows from the left table and matches rows from the right table.
• Right Join (Right Outer Join): Returns all rows from the right table and matched rows from the left table.
• Full Join (Full Outer Join): Returns all rows when there is a match in either table.

Question No 6. How do you optimise a SQL query?

Answer: Optimising a SQL query includes several techniques. These techniques are:

• Indexing: Use indexes on columns that are frequently searched or used in joins.
• Query Refactoring: Rewrite complex queries to simplify the logic.
• Join Optimisation: Use appropriate join types and conditions to reduce the number of rows.
• Limiting Results: Use LIMIT or TOP clauses to restrict the number of rows.

Question No 7. What is Apache Hadoop?

Answer: Apache Hadoop is an open-source framework designed for processing large datasets. It consists of two main components: Hadoop Distributed File System (HDFS) for storage and MapReduce for processing. HDFS stores data in multiple nodes. MapReduce, on the other hand, processes data in parallel, breaking tasks into smaller subtasks. Hadoop is widely used for big data analytics.

Question No 8. Explain the MapReduce framework.

Answer: MapReduce is a programming model for processing large datasets parallelly in a cluster. It consists of two main functions:

• Map: The input data is divided into smaller chunks, and each chunk is processed by a mapper function that transforms the data into key-value pairs.
• Reduce: The key-value pairs are then shuffled and sorted by key, and the reducer function processes each key’s associated values to produce the final output.

Question No 9. What is Apache Spark?

Answer: Apache Spark is an open-source computing system that provides an advanced analytics engine for big data processing. It supports various data processing tasks, including batch processing, machine learning, and graph processing. Spark uses in-memory computing, which makes it significantly faster than traditional disk-based processing systems like Hadoop. Spark’s components include Spark SQL, Spark Streaming, MLlib, and GraphX, which enhance its functionality for different use cases.

Question No 10. What are the differences between Hadoop and Spark?

Answer: The differences between Hadoop and Spark are mentioned below:

• Processing Model: Hadoop uses disk-based MapReduce for batch processing, while Spark uses in-memory computing for faster processing.
• Speed: Spark is generally faster than Hadoop due to its in-memory processing capabilities.
• Ease of Use: Spark provides higher-level APIs in multiple languages and has a more user-friendly interface compared to Hadoop’s MapReduce.
• Data Processing: Hadoop is suitable for batch processing, while Spark supports both batch and practical data processing.
• Components: Spark includes libraries for machine learning, graph processing, and streaming, whereas Hadoop primarily focuses on storage (HDFS) and batch processing (MapReduce).

Question No 11. What is a NoSQL database?

Answer: A NoSQL database is a non-relational database designed to handle large volumes of unstructured or semi-structured data. Unlike traditional SQL databases, NoSQL databases do not use fixed table schemas. NoSQL can store data in various formats, such as key-value pairs, documents, wide-columns, or graphs. They can handle large-scale data workloads efficiently. NoSQL databases are ideal for applications which require flexible data models, high throughput, and low-latency access. 

Question No 12. What is data partitioning?

Answer: Data partitioning is the process of dividing a large database into smaller parts. Each part can be stored separately. Partitioning is done based on ranges of values, list values, or hashing. This technique enhances query performance and allows the database to access only relevant parts instead of scanning the whole dataset. Data partitioning is commonly used in large-scale databases to optimise storage and access.

Question No 13. Explain the concept of data sharding.

Answer: Data sharding is a database pattern in which data is horizontally divided between multiple servers or nodes. Sharding improves the system’s scalability and performance. It also helps databases handle more traffic by lessening the data and query load. This approach is especially useful for handling large volumes of data and high-throughput applications. 

Question No 14. What is data replication?

Answer: Data replication means creating and maintaining multiple copies of data in different systems. The primary purpose is to ensure data availability and reliability. If one copy of the data is unavailable due to hardware failure or network issues, other copies can be accessed. Replication can be synchronous or asynchronous, depending on the requirements. Data replication also helps in load balancing and system recovery. It ensures data integrity and accessibility.

Question No 15. What are indexes in databases?

Answer: Indexes in databases are special data structures that improve the speed of data recovery. They work like an index in a book, allowing the database to find rows quickly without scanning the whole table. Indexes are created on columns that are frequently searched or used. Common types include B-tree, hash, and bitmap indexes. Proper indexing is essential for optimising query performance and maintaining database efficiency.

Question No 16. What is a primary key?

Answer: A primary key is a unique identifier for each record in a database table. It ensures that each row can be uniquely identified and prevents duplicate entries. A primary key can be a single column or a combination of multiple columns. Primary keys are essential for tables in a relational database. They are indexed to speed up query performance. 

Question No 17. What is a foreign key?

Answer: A foreign key is a column or a set of columns in one table that uniquely identifies rows in another table. It connects two tables, linking the foreign key in the child table and the primary key in the parent table. Foreign keys ensure the value in the child table matches a valid value in the parent table. They help in complex queries that include joining tables based on related data. They are crucial for maintaining a relational database structure.

Question No 18. What is a data lake?

Answer: A data lake is a centralised vault that allows you to store all your structured and unstructured data. It can store raw data, such as logs, JSON, XML, and binary data, for future processing and analysis. Data lakes support a wide range of data types and sources. They help in advanced analytics, machine learning, and real-time data processing. Data lakes are designed to handle large volumes of data. They differ from data warehouses, which are structured and optimised for query performance.

Question No 19. What is the purpose of data modelling?

Answer: Data modelling is the process of creating a visual representation of a complex data system. It helps design and structure data according to business requirements. The purpose is to define and organise data elements, their connections, and rules within a database. Data modelling ensures consistency, accuracy, and clarity in data management. It helps in the development of databases, data warehouses, and data lakes. Proper data modelling improves database performance, simplifies maintenance, and enhances data integrity and security.

Question No 20. Explain the difference between OLAP and OLTP.

Answer: OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) serve different purposes in data management. OLTP systems are designed to handle daily transactions and data modifications. They focus on speed and efficiency for insert, update, and delete operations, such as those in online banking and e-commerce systems. OLAP systems, on the other hand, are optimised for complex queries and data analysis. OLAP is used in business intelligence and reporting. 

Question No 21. Why do you want to become a data engineer?

Answer: I want to become a data engineer because I enjoy working with data and solving complex problems. Data engineering allows me to design and build systems that handle large volumes of data efficiently. I am fascinated by how data can drive decision-making and business insights. This field offers continuous learning and growth opportunities, which keeps me motivated. Additionally, data engineering is important for supporting data science and analytics, making it a valuable and impactful career.

Question No 22. Describe a challenging project you worked on and how you handled the challenges.

Answer: In one challenging project, I transferred a legacy data warehouse to a modern cloud-based solution. The existing system was outdated, and data inconsistencies were frequent. I started by thoroughly understanding the old system and identifying the issues. I then designed a new system that addressed these problems, ensuring scalability and performance. Collaborating with team members, I set up a detailed transfer plan and target. We did thorough testing and validation to ensure data accuracy. Despite tight deadlines and unexpected hurdles, our teamwork led to a successful transfer.

Question No 23. How do you prioritise tasks when working on multiple projects?

Answer: When working on multiple projects, I prioritise tasks by assessing their urgency and impact. I use project management tools like Trello or Asana to keep track of deadlines and progress. I communicate regularly with stakeholders to understand their priorities and adjust my schedule accordingly. Time-blocking techniques help me focus on one task at a time, reducing distractions. Regularly reviewing and adjusting my task list ensures I stay aligned with project targets and deadlines.

Question No 25. How do you handle tight deadlines?

Answer: To handle tight deadlines, I first prioritise tasks based on their urgency and importance. I create a clear action plan with specific milestones and deadlines. Effective time management and avoiding distractions are crucial, so I focus on one task at a time. I communicate with my team and stakeholders to manage expectations and request support if needed. Staying organised and using productivity tools helps me track progress and stay on schedule. When necessary, I am willing to put in extra hours to ensure timely completion without compromising quality.

Question No 26. Describe a time when you had to learn a new tool or technology quickly.

Answer: In a recent project, I needed to learn Apache Kafka for data streaming. I started by searching and reading documentation to understand the basics. I enrolled in the Digital Regenesys’ online course to gain theoretical knowledge and practical experience. I asked my colleagues who had experience with Kafka, which provided valuable insights. By dedicating focused time and effort, I quickly became proficient and successfully implemented Kafka in our data pipeline. This experience improved my ability to adapt and learn new technologies efficiently.

Question No 27. How do you ensure data quality in your projects?

Answer: To ensure data quality in my projects, I use validation checks at various stages of data pipeline to catch errors early. Regularly monitoring and auditing data helps identify and address inconsistencies or anomalies. I use data profiling tools to analyse data and ensure it meets quality criteria. Collaborating with stakeholders ensures a clear understanding of data quality.

Question No 28. Describe a situation where you had to fix a data pipeline issue.

Answer: A data pipeline fails due to a sudden surge in data volume. It causes delays in data processing. I began by using monitoring tools and log analysis. Once pinpointed the issue, I optimised the affected components, such as increasing buffer sizes and parallel processing capabilities. I also communicated with the team to ensure they were aware of the issue and the steps being taken. The response and optimisation restored the pipeline’s performance, minimising disruption.

Question No 29. How do you approach learning new programming languages or tools?

Answer: When learning new programming languages or tools, I start by understanding their basic concepts and use cases. Online tutorials, courses, and documentation provide a good foundation. Practice through small projects or coding exercises helps improve learning. I participate in online communities and forums to seek advice and share experiences. Consistent practice and experimentation are key to gain proficiency. Additionally, I stay updated with best practices in technology to improve my skills continuously.

Question No 30. What motivates you to work in data engineering?

Answer: I am motivated to work in data engineering because it allows me to solve complex problems and create efficient data systems. The impact of my work on business decisions and insights is highly rewarding. I enjoy the continuous learning and evolution in this field. It keeps me engaged and challenged. Collaborating with teams and contributing to innovative projects motivates me a lot. Data engineering skills align with my strengths and interests. The potential for career growth and making a meaningful impact drives my passion for this field.

Question No 31. How would you design a data pipeline for a new project?

Answer: To design a data pipeline for a new project, I start by understanding the project requirements and data sources. I then outline the stages of the pipeline, including data ingestion, processing, and storage. Choosing appropriate technologies for each stage is important, such as Kafka for ingestion and Spark for processing. I ensure data quality by implementing validation and cleansing steps. Designing for scalability and performance, I consider data volume and processing speed. Finally, I set up monitoring and logging to detect and address.

Question No 32. How do you stay updated with the latest trends in data engineering?

Answer: I stay updated with the latest trends in data engineering by regularly reading industry blogs and publications, such as Medium, Towards Data Science, and TechCrunch. I follow influencers and leaders on social media platforms like Fcebook and Twitter. I also attend webinars, conferences, and workshops to learn from experts. Online courses and certifications help me gain new skills and stay updated with emerging technologies. Continuous learning is essential to remain competitive in this rapidly evolving field.

Question No 33. What are some common data quality issues?

Answer: Some common data quality issues are missing data, where essential information is absent from records. Inaccurate data arises from errors in data entry or measurement. Inconsistent data occurs when data formats or values differ in datasets. Duplicate data occurs when the same record appears multiple times. Finally, data integration issues can arise when merging data from different sources.

Question No 34. How do you handle missing data?

Answer: Handling missing data includes several strategies depending on the context and extent of the missingness. One approach is to remove records with missing values if they represent a small portion of the dataset. Imputation techniques, such as filling missing values with the mean, median, or mode, can be used. More sophisticated methods include using machine learning models to predict missing values. Regularly reviewing and validating the dataset helps maintain data quality.

Question No 35. What is data governance?

Answer: Data governance is the framework of policies and procedures for managing an organisation’s data assets. It ensures data quality, consistency, and security in the organisation. Data governance ensures rules for data access, usage, and compliance with regulations. It also includes monitoring and auditing data practices. Effective data governance ensures better decision-making and risk management.

Question No 36. What is data lineage?

Answer: Data lineage tracks the flow of data from its origin to its final destination. It documents the data’s journey, including transformations, processes, and storage locations. Data lineage provides transparency and traceability. It helps understand how data is derived and modified. It also helps in fixing data issues by highlighting where errors occur in the pipeline. Ensuring data lineage supports regulatory compliance by demonstrating data handling practices. It also improves data governance and facilitates better decision-making by providing a clear data history.

Question No 37. What is data sharding?

Answer: Data sharding is the process of dividing a large dataset into smaller, more manageable parts called shards. Each shard is stored on a different database server. Sharding helps scale databases horizontally to ensure growth in data volume and user load. It includes defining a sharding key to distribute data evenly across shards. Managing data consistency and ensuring efficient queries are important in a shared database. Sharding is commonly used in database systems to enhance scalability and reliability.

Question No 38. What is data ingestion?

Answer: Data ingestion is the process of collecting and importing data from various sources into a data storage system. It includes extracting data from different formats and locations, such as databases, APIs, or files. Data ingestion ensures data quality and consistency for accurate analysis. Data ingestion pipelines handle the transformation and loading of data into the target storage, such as data lakes or warehouses. Effective data ingestion is important for timely and reliable data access for analysis and decision-making.

Question No 39. What is a REST API?

Answer: A REST API (Representational State Transfer Application Programming Interface) is a set of rules for creating web services. It allows different software applications to communicate over HTTP using standard methods such as GET, POST, PUT, and DELETE. REST APIs use URLs to access resources and often return data in formats like JSON or XML. They enable seamless integration between different systems and services, allowing applications to recover and manipulate data easily. REST APIs are widely used due to their simple format and scalability.

Question No 40. What is data replication?

Answer: Data replication is the process of copying and maintaining data in multiple storage systems. This ensures that the same data is available in different systems, providing redundancy and enhancing data availability. Replication can be synchronous, where changes are mirrored in real-time, or asynchronous, where updates are applied after a delay. It helps with data backup and load balancing by distributing data across multiple servers or databases.

In-demand Data Engineer Job Profiles for Freshers

There are several in-demand job profiles for freshers in the data engineering field. Some of those roles are Junior Data Engineer, ETL Developer, Data Analyst, etc. Junior Data Engineers design, develop, and maintain data pipelines to collect, transform and store large volumes of data. They use tools like SQL, Python, Hadoop, Spark, etc. ETL (Extract, Transform, Load) Developers work on moving data from various sources to a data warehouse. They use ETL tools like Talend or Informatica to ensure data is accurately extracted, properly transformed, and efficiently loaded.

A career in data engineering can be rewarding and challenging. As a data engineer, your role will be vital to an organisation’s success. You will provide easy access to data for data scientists, analysts, and decision-makers. You’ll use your programming and problem-solving skills to create scalable solutions. According to the payscale report, the average salary of an entry-level data engineer with less than 1 year of experience is R305,446. This salary includes tips, bonuses, and overtime pay. The salary increase to R417,235 with 1-4 years of experience. Check the following table to know how experience influences the salary range.

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Experience	Salary
Less than 1 year	R305,446
1-4	R417,235
5-9	R609,486
10-19	R865,543
20 years and higher	R900,000

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Data engineering offers various in-demand job profiles for freshers like Junior Data Engineer, ETL Developer, and Data Analyst. These roles involve designing and maintaining data pipelines, handling large datasets, and ensuring data quality. The demand for skilled data engineers is rising with the growing importance of data in business decision-making. Digital Regenesys offers a comprehensive curriculum to improve the skills in this field. Digital Regenesys also offer an online learning approach with the tools required to succeed in the dynamic field of Data engineering. After completing your training, you will be well-prepared to tackle any challenges and contribute effectively to industry demands.

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