If I were planning for Data Engineering interviews in the upcoming months then I will prepare this way ⛵



1. Learn important SQL concepts
Go through all key topics in SQL like joins, CTEs, window functions, group by, having etc.

2. Solve 50+ recently asked SQL queries
Practice queries from real interviews. focus on tricky joins, aggregations and filtering.

3. Solve 50+ Python coding questions
Focus on:

List, dictionary, string problems, File handling, Algorithms (sorting, searching, etc.)


4. Learn PySpark basics
Understand: RDDs, DataFrames , Datasets & Spark SQL


5. Practice 20 top PySpark coding tasks
Work on real coding examples using PySpark -data filtering, joins, aggregations, etc.

6. Revise Data Warehousing concepts
Focus on:

Star and snowflake schema
Normalization and denormalization


7. Understand the data model used in your project
Know the structure of your tables and how they connect.


8. Practice explaining your project
Be ready to talk about: Architecture, Tools used, Pipeline flow & Business value


9. Review cloud services used in your project
For AWS, Azure, GCP:
Understand what services you used, why you used them nd how they work.

10. Understand your role in the project
Be clear on what you did technically . What problems you solved and how.

11. Prepare to explain the full data pipeline
From data ingestion to storage to processing - use examples.

12. Go through common Data Engineer interview questions
Practice answering questions about ETL, SQL, Python, Spark, cloud etc.

13. Read recent interview experiences
Check LinkedIn , GeeksforGeeks, Medium for company-specific interview experiences.

14. Prepare for high-level system design
questions.