Data engineering is a rapidly growing field that has become an integral part of businesses across various industries. If you're a fresher or young professional preparing for interviews for data engineering jobs, here's a list of the 25 most-asked data engineering interview questions along with brief sample answers.
1. What is the role of a data engineer in an organisation?
A data engineer is responsible for designing, constructing, and maintaining the systems and infrastructure needed to store, process, and analyse large volumes of data. They work closely with data scientists and analysts to ensure that data pipelines are efficient and reliable.
2. How do you handle missing or corrupted data in a dataset?
One approach is to remove the affected rows or columns if they have a significant impact on the analysis. Another option is to impute missing values using techniques such as mean imputation or regression imputation.
3. Can you explain the concept of data normalisation?
Data normalisation involves organising data into tables and reducing redundancy by eliminating duplicate information. It helps improve database efficiency and reduces the chances of anomalies during data manipulation.
4. What is ETL?
ETL stands for Extract, Transform, and Load. It refers to the process of extracting raw data from various sources, transforming it into a consistent format, and loading it into a target system for analysis.
5. How would you optimise a database query that is running slowly?
There are several ways to optimise a slow-running query, such as by creating appropriate indexes, rewriting the query using more efficient logic, or partitioning tables to distribute the load evenly.
6. Describe your experience with big data technologies like Hadoop and Spark.
Highlight any hands-on experience you have with tools like Hadoop Distributed File System (HDFS) or Apache Spark for processing large-scale datasets efficiently.
7. Can you explain the difference between structured and unstructured data?
Structured data refers to information that is organised in a predefined format, such as tables in a relational database. Unstructured data refers to information that does not have a predefined structure, like text documents or social media posts.
8. How would you handle a situation where there is too much data to fit into memory?
One approach is to use distributed computing frameworks like Apache Hadoop or Apache Spark, which can handle large-scale data by distributing it across multiple machines. Another option is to use techniques like sampling or parallel processing.
9. What is the difference between batch processing and real-time processing?
Batch processing involves processing a large volume of data at once, typically at scheduled intervals. Real-time processing, on the other hand, involves processing data as it arrives, allowing for immediate analysis and decision-making.
10. How would you ensure data quality in a project?
Data quality can be ensured through various techniques, such as data profiling, data cleansing, and validation checks. Regular monitoring and validation of data sources are also important.
11. Have you worked with any cloud-based data storage solutions? If yes, please elaborate.
If you have experience with platforms like Amazon Web Services (AWS) S3 or Google Cloud Storage, explain how you used these services to store and retrieve data efficiently.
12. Describe your experience with SQL programming.
Highlight your proficiency in writing complex SQL queries for data extraction, transformation, and analysis.
13. Can you explain the concept of schema evolution in the context of database design?
Schema evolution refers to the process of modifying a database schema over time while preserving existing data integrity. It involves making changes to tables' structure, adding or removing columns, etc., without affecting existing applications that rely on the database.
14. What is the importance of metadata in a data engineering project?
Metadata provides information about the characteristics of data, such as its source, format, meaning, and relationships with other datasets. It helps in understanding and managing complex datasets effectively.
15. How do you ensure security in a data engineering project?
Security measures can include using encryption techniques to protect sensitive information, implementing role-based access controls (RBAC), and regularly updating software patches to prevent vulnerabilities.
16. Explain the concept of partitioning in database management systems.
Partitioning involves dividing a large table into smaller, more manageable pieces based on defined criteria. It helps improve query performance and simplifies data management.
17. Have you used any data integration tools? If yes, please provide examples.
Mention any experience you have with tools like Apache Kafka, Talend, or Informatica PowerCenter for integrating data from various sources into a centralised system.
18. Describe your experience with data warehousing.
If you have worked with data warehousing technologies like Amazon Redshift or Google BigQuery, explain how you designed and managed data warehouses for efficient analytics.
19. Can you explain the concept of data lineage?
Data lineage refers to tracking the origin and movement of data from its source to its destination. It helps in understanding the path taken by the data and ensures its integrity throughout the process.
20. How do you handle sensitive or confidential information in a data engineering project?
Explain your approach to ensuring compliance with relevant privacy regulations, such as anonymizing or pseudonymizing data, implementing access controls, and regularly auditing access logs.
21. What are some best practices for designing scalable data pipelines?
Some best practices include using parallel processing techniques, implementing fault-tolerant mechanisms, optimising resource utilisation, and monitoring pipeline performance using appropriate tools.
22. Can you explain the concept of ACID properties in database transactions?
ACID stands for atomicity, consistency, isolation, and durability—four properties that ensure reliable transaction processing in databases.
23. Have you worked with any NoSqL databases? If yes, please elaborate.
If you have experience with databases like MongoDB or Cassandra, mention how you used them to handle unstructured or semi-structured data efficiently.
24. How do you handle changes in source systems that impact existing ETL processes?
Explain how you would assess the impact of such changes, modify existing ETL processes accordingly, and ensure minimal disruption to downstream systems.
25. Describe your experience with data visualisation tools.
If you have used tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn, explain how you used them to create meaningful visualisations from raw data.
Mastering Data Engineering Interview Questions
By familiarising yourself with common interview questions and preparing well-crafted answers, you can confidently showcase your skills and knowledge to potential employers. Remember to explore WiZR for expert career guidance and financing options to kickstart your data engineering career journey.
Also read - How to become a Data Engineer
FAQs
1. How much do data engineers earn?
Data engineers in India can earn an average salary of ₹8–14 lakhs per year, depending on their experience and expertise.
2. Who hires data engineers?
Data engineers are in high demand across various industries, such as technology, finance, healthcare, e-commerce, and consulting firms.
3. What skills are essential for data engineers?
Essential skills for data engineers include proficiency in programming languages like Python or SQL, database management systems (DBMS), ETL (Extract, Transform, Load) processes, data warehousing concepts, and cloud platforms like AWS or Azure.
4. How can I become a data engineer?
To become a data engineer, you can pursue a bachelor's or master's degree in computer science or a related field. Additionally, acquiring certifications in relevant technologies like big data or cloud computing can enhance your prospects.
