Top 30 Data Engineer Interview Questions 2025 | Data Engineer Interview Preparation | Intellipaat

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Preparing for a Data Engineer interview in 2025? You're in the right place! This video covers the Top 30 Data Engineer Interview Questions to help you ace your next job interview. Whether you're a fresher or have a few years of experience, we've got you covered. We'll explore key topics like data engineer interview questions for freshers, Python-based questions, and critical data engineering concepts to help you stay ahead in your interview preparation.

From data pipelines to big data frameworks, you'll get the insights you need to feel confident. We’ve also included tips on how to prepare for a data engineer interview with a focus on real-world scenarios and practical solutions. Whether you're wondering about data engineer interview preparation or looking for questions with 2 years of experience in mind, this video will provide the answers you need.

Watch now to ensure you're fully equipped for your next interview, and don’t forget to like, subscribe, and hit the notification bell for more expert interview preparation content from Intellipaat!

📕 Below are the questions covered in this 'Data Engineer Interview Questions' Video:
🥇 00:00:00 - Introduction to Data Engineer Interview Questions

👨‍💻 Data Engineer Interview Questions for Freshers (Basic Level Data Engineer Interview Questions):
01:20 - Q1. What is Data Engineering?
05:07 - Q2. What is Normalization?
07:39 - Q3. Define data modeling.
10:25 - Q4. Differentiate between Structured and unstructured data
11:24 - Q5. What are ETL and ELT pipelines?
13:51 - Q6. Explain the difference between relational databases (RDBMS) and NoSQL databases.
15:36 - Q7. What is the purpose of indexing in databases, and how does it improve performance?
17:52 - Q8. What are the common challenges in data engineering, and how do you address them?
19:32 - Q9. What is a primary key and a foreign key in relational databases? Why are they important?
21:56 - Q10. Explain the concept of ACID properties in database transactions.

👨‍💻 Intermediate Level Data Engineer Interview Questions:
24:21 - Q11. What is data partitioning, and why is it used in large-scale data systems?
25:56 - Q12. How do you handle large datasets in Python that do not fit into memory? 27:09 - Q13. What is a star schema and a snowflake schema in data modeling?
29:08 - Q14.What are the differences between batch processing and stream processing?
31:35 - Q15. What is a data pipeline, and what are its essential components?
33:34 - Q16. What is a distributed file system? How does HDFS (Hadoop Distributed File System) work?
35:43 - Q17. What are the main advantages and disadvantages of using cloud-based data platforms (e.g., AWS, Azure, GCP)?
38:42 - Q18. Explain the working of Apache Kafka
40:10 - Q19. Explain the 4 V’s of Big Data
43:19 - Q20. How do you ensure data quality and data integrity in your pipelines?

👨‍💻 Advanced Level Data Engineer Interview Questions for Experienced:
45:44 - Q21. Differences between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) systems?
47:44 - Q22. Explain how the name node communicates with the data node?
49:52 - Q23. What is COSHH?
50:31 - Q24. Explain the purpose of MapReduce and how it works
52:39 - Q25. Name the XML configuration files in Hadoop
53:58 - Q26. What are the major components in the Hive Data Model?
54:47 - Q27. Is it possible to create multiple tables for individual data files?
56:01 - Q28. What is data skew, and how can it affect distributed data processing?
57:52 - Q29. What are the various modes in Hadoop?
58:46 - Q30. How can you ensure data security in Hadoop?

➡️ About the Course
Our Data Engineering course covers SQL, Python, data pipelines, Spark, and AWS/Azure cloud services. With real-world projects, you'll master ETL, data sourcing, cloud data warehouses, and Data Modeling.

➡️Who should take this course?
👉🏼 Freshers and Undergraduates willing to pursue a career in data engineering
👉🏼 Anyone looking for a career transition to data engineering
👉🏼 IT professionals
👉🏼 Experienced professionals willing to learn data engineering

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