Top 20+ Data Engineer Interview Questions with Expert Answers!

When you're interviewing for a data engineer role, expect a mix of SQL challenges, data pipeline design, big data tools, and scenario-based questions. These test how you think under pressure. 

You might be asked to build an ETL workflow, write a complex query, or explain how you'd fix a failing pipeline. 

This blog covers over 20 common data engineer interview questions, along with expert-backed answers. They don’t just tell you what to say, but how to think through them. Read on to feel ready, not just rehearsed.

20+ Core Data Engineer Interview Questions

At a company like Netflix, a data engineer might be tasked with building a real-time data pipeline that processes millions of user interactions every second to personalize recommendations. 

During interviews, candidates are asked how they’d structure such pipelines, handle failures, ensure low-latency data delivery, and store data efficiently. Expect practical questions around distributed systems, SQL performance, cloud tools, and scalable architecture.

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In this section, we’ve grouped 20+ interview questions by topic, covering SQL, pipelines, big data, cloud, and more, to help you prepare with focus.

SQL and Database Data Engineer Interview Questions

SQL is at the core of every data engineer’s toolkit. Interviews often test your ability to write efficient queries, design scalable schemas, and solve business problems with data. Here are three must-know questions that regularly come up.

1. Write a SQL query to find the second-highest salary.

How to answer:

• Mention using LIMIT, OFFSET, or subqueries.
• Show awareness of handling duplicates.
• Mention alternative methods like DENSE_RANK() or MAX(<subquery>)

Sample Answer:
To find the second-highest salary, you can use a subquery that selects the maximum salary less than the highest one. Example:

SELECT MAX(salary)  
FROM employees  
WHERE salary < (SELECT MAX(salary) FROM employees);

This works well when there are duplicate salaries. Alternatively, in databases supporting window functions, you can use DENSE_RANK() for more control.

2. Explain the difference between INNER JOIN, LEFT JOIN, and OUTER JOIN.

How to answer:

• INNER JOIN returns only matching records
• LEFT JOIN returns all from left + matched from right
• OUTER JOIN includes all records from both, with NULLs for missing matches

Sample Answer:
INNER JOIN returns rows that match in both tables. LEFT JOIN includes all records from the left table and matches from the right; unmatched right-side rows return as NULL. 

FULL OUTER JOIN returns all records from both sides, filling NULLs where there’s no match. Use INNER JOIN for filtering, LEFT JOIN to preserve unmatched left records, and OUTER JOIN when you need everything.

3. What is normalization? What are the different normal forms?

How to answer:

• Define normalization as reducing data redundancy
• Briefly name 1NF to 3NF (at least)
• Mention purpose: data integrity and efficiency

Sample Answer:
Normalization is the process of structuring a relational database to minimize redundancy and dependency. It involves organizing data into multiple related tables. The main normal forms are:

• 1NF: Eliminate repeating groups
• 2NF: Remove partial dependencies
• 3NF: Remove transitive dependencies
  This helps maintain consistency and makes updates easier without affecting data accuracy.

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Also Read: How to Become a Big Data Engineer: 8 Steps, Essential Skills, and Career Opportunities for 2025

Now that SQL fundamentals are covered, let’s move into data modeling and warehousing, key areas for building scalable, analysis-ready data systems.

Data Modeling & Warehousing Data Engineer Interview Questions

Data modeling and warehousing questions check if you can organize data for both performance and clarity. Expect scenarios on schema design, storage optimization, and dimensional models. Here are a few core questions interviewers commonly ask.

4. What is a star schema vs snowflake schema?

How to answer:

• Define both schemas briefly
• Highlight structure differences
• Mention performance and use case impact

Sample Answer:
A star schema has a central fact table linked directly to denormalized dimension tables, making it simpler and faster for queries. 

In contrast, a snowflake schema normalizes the dimensions into multiple related tables, which reduces redundancy but can slow performance. Star schemas are often used in BI tools for speed, while snowflake schemas offer better data integrity and storage efficiency.

5. How would you design a data warehouse for an e-commerce platform?

How to answer:

• Identify key business entities: orders, users, products, etc.
• Use fact and dimension tables
• Choose schema type and explain ETL flow

Sample Answer:
For an e-commerce platform, I’d create a star schema with a central Sales Fact table linked to dimensions like Customer, Product, Time, and Region. This allows for fast sales and user behavior analysis. ETL processes would clean and load transactional data into the warehouse, with regular refresh intervals to keep analytics up to date.
Also Read: Top 15 Types of Data Visualization: Benefits and How to Choose the Right Tool for Your Needs in 2025

6. What are fact and dimension tables?

How to answer:

• Define each clearly
• Give examples
• Explain how they relate

Sample Answer:
Fact tables store measurable data like revenue, quantity sold, or clicks. Dimension tables store descriptive information like customer names, product categories, or regions. In a retail schema, a Sales Fact table might store product_id, customer_id, and sales_amount, while the Product and Customer dimensions provide detailed context. Together, they support multi-angle analysis.

With data structures in place, the next step is moving and transforming data efficiently. Let’s now look at data pipeline and ETL questions that test how well you can build and maintain reliable data workflows.

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Also Read: Top 5 Online Data Engineering Courses & Certifications [2025]

Data Pipelines & ETL Data Engineer Interview Questions

Data pipeline and ETL questions focus on how to reliably and efficiently move raw data into structured systems at scale. Interviewers want to see how you handle workflow design, failure recovery, and tool selection in real projects.

7. How do you design an end-to-end data pipeline?

How to answer:

• Start with data source and destination
• Mention ingestion, transformation, validation, and load
• Include scheduling, monitoring, and error handling

Sample Answer:
I begin by identifying the data source, like transactional databases or APIs. Data is ingested using tools like Apache Kafka or custom scripts, processed through an ETL layer (Apache Spark or Python), validated, and then loaded into a data warehouse, such as Snowflake or BigQuery. I use Airflow to schedule and monitor jobs, and include retry logic and alerts for failures.

8. What tools have you used for ETL? (Airflow, Informatica, etc.)

How to answer:

• List key tools and why they were used
• Mention both open-source and enterprise tools if applicable
• Highlight how you used them in projects

Sample Answer:
I’ve used Apache Airflow for building and managing ETL workflows due to its flexibility and DAG-based structure. In one project, I used Informatica for enterprise-level ETL involving high-volume data transformations. I also use dbt for data modeling and transformation, and Python scripts for custom processing tasks. Tool choice often depends on scale, team familiarity, and integration needs.

9. How do you handle pipeline failures?

How to answer:

• Mention monitoring and alerting
• Talk about retries, fallbacks, and logging
• Share real example if possible

Sample Answer:
I handle failures by implementing detailed logging and setting up alerts using tools like Prometheus or Airflow’s built-in email/SMS triggers. Pipelines include retry mechanisms with backoff strategies. 

For example, in a batch pipeline with S3 ingestion, I added checkpointing to resume processing from the last successful record. Root cause analysis and proper documentation are also part of the recovery process.

Once your pipelines are in place, the next challenge is handling massive volumes of data efficiently. Let’s move on to big data technologies, where questions often focus on tools like Hadoop, Spark, and Kafka.

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Big Data Technologies Data Engineer Interview Questions

Big data questions test your ability to work with large-scale data processing frameworks and distributed systems. Interviewers want to know if you can choose the right tools, optimize performance, and manage data across clusters. Expect questions on Spark, Hadoop, Kafka, and how they fit into modern data workflows.

10. Compare Hadoop and Spark.

How to answer:

• Mention data processing models (batch vs in-memory)
• Compare speed and use cases
• Touch on ease of use and APIs

Sample Answer:
Hadoop uses a batch processing model and stores data on disk between each operation, which makes it slower. Spark, on the other hand, processes data in-memory, offering much faster performance for iterative and real-time tasks. 

While Hadoop is suited for long-running jobs on massive datasets, Spark is preferred for complex analytics, machine learning, and streaming use cases. Spark also supports more user-friendly APIs in Python, Scala, and SQL.

11. What is the role of Kafka in a data engineering workflow?

How to answer:

• Define Kafka as a distributed messaging system
• Mention data streaming and decoupling services
• Explain integration with other tools

Sample Answer:
Kafka acts as a real-time data streaming platform that decouples data producers and consumers. It’s used to ingest large volumes of data from various sources—such as logs, sensors, or APIs—and stream them to processing engines like Apache Spark or storage systems like Apache HDFS. In one project, I used Kafka to stream user click data into Spark Streaming for near real-time analytics.

12. How would you handle streaming data?

How to answer:

• Mention tools like Spark Streaming, Flink, Kafka
• Talk about message durability and fault tolerance
• Highlight windowing, latency, and throughput

Sample Answer:
To handle streaming data, I’d use tools like Kafka for ingestion and Spark Streaming or Apache Flink for processing. I’d set up checkpoints to ensure fault tolerance and use sliding or tumbling windows for real-time aggregations. 

Monitoring lag and throughput is key to tuning performance. In a past project, I used Spark Structured Streaming to process live order data and update dashboards with sub-second latency.

Also Read: How to Become a Data Engineer: 9 Steps, Key Skills, and Career Prospects for 2025

Beyond tools and platforms, data engineers are expected to write clean, efficient code for automation, data transformation, and system integration. Let’s move into programming and scripting questions that test your coding logic and problem-solving approach.

Programming and Scripting Data Engineer Interview Questions

Programming and scripting skills are essential for automating data tasks, cleaning datasets, and building custom workflows. Interviewers often test your ability to write efficient code, debug issues, and choose the right language for the job. Here are some common questions that assess your hands-on coding capabilities.

13. Which languages do you use for data engineering tasks?

How to answer:

• List your primary languages (Python, SQL, etc.)
• Mention what you use them for (ETL, automation, API calls, etc.)
• Highlight language choice based on task complexity and performance

Sample Answer:
I primarily use Python for building ETL workflows, data validation, and automation tasks due to its rich ecosystem of libraries like Pandas, PySpark, and Airflow. I use SQL extensively for querying and transforming structured data, and occasionally Shell scripting for job orchestration. In some cases, I’ve worked with Scala in Spark-based environments for better performance.

14. Python vs Scala: when and why?

How to answer:

• Compare ease of use vs performance
• Mention ecosystem and team adoption
• Highlight Spark-related context

Sample Answer:
Python is great for its readability, large number of data libraries, and quicker development. I prefer it for prototyping, smaller ETL tasks, and ML pipelines. Scala is more performance-oriented and integrates natively with Apache Spark, so I use it when working with large-scale distributed data or production-level Spark jobs. The choice depends on the project's performance needs and team expertise.

15. Write a Python script to clean a dataset with missing values.

How to answer:

• Show simple code using Pandas
• Mention strategies: drop or fill
• Keep it clean and realistic

Sample Answer:
Here’s a basic script using Pandas to clean missing values:

import pandas as pd
# Load dataset
df = pd.read_csv("data.csv")
# Drop rows with any missing values
df_cleaned = df.dropna()
# Or fill missing values with default
# df_cleaned = df.fillna({'age': 0, 'income': df['income'].mean()})
print(df_cleaned.head())

This script loads the data, drops rows with nulls, or optionally fills them with defaults like zero or column means.

Once your code works well on small datasets, the next test is scaling it for millions of records and real-time demands. Let’s move on to system design and scalability questions that explore how you build a reliable, high-performance data infrastructure.

System Design & Scalability Data Engineer Interview Questions

These questions dig into how you design data systems that can handle scale, speed, and failure without breaking. Interviewers want to know if you can think beyond code, considering architecture, data flow, storage, and fault tolerance. Here are some key questions that reveal how you approach large-scale system challenges.

16. Design a real-time analytics platform.

How to answer:

• Start with data ingestion and streaming tools
• Mention processing engine, storage, and data visualization
• Talk about scalability, fault tolerance, and latency

Sample Answer:
To design a real-time analytics platform, I’d use Kafka for streaming data ingestion, Spark Structured Streaming or Flink for processing, and store results in a low-latency database like Apache Druid or Elasticsearch. 

For dashboards, I’d use Grafana or Superset. I'd ensure horizontal scaling, implement checkpointing for recovery, and use partitioned storage to handle growing volumes with minimal delay.

17. How would you design a system that supports billions of daily transactions?

How to answer:

• Break down into ingestion, processing, storage, and access layers
• Focus on high availability and fault tolerance
• Mention use of distributed systems and load balancing

Sample Answer:

For handling billions of daily transactions, I’d design a distributed architecture using load balancers, Kafka for ingestion, and Spark or Flink for real-time processing. Storage would be split across columnar warehouses like BigQuery or Redshift and NoSQL stores for fast lookups. I’d also use partitioning, sharding, and caching (like Redis) to ensure fast response times and resilience under heavy load.

18. What is data partitioning, and how does it help with performance?

How to answer:

• Define partitioning
• Explain how it reduces query time
• Mention impact on parallelism and storage

Sample Answer:
Data partitioning means dividing a large dataset into smaller, manageable chunks based on keys like date, region, or ID. This improves performance by allowing queries to scan only the relevant partitions instead of the whole dataset. 

It also enables parallel processing, which speeds up ETL and analytics tasks. In distributed systems, partitioning helps balance load across nodes and reduces bottlenecks.

Also Read: Top 15 Data Visualization Project Ideas: For Beginners, Intermediate, and Expert Professionals

 Let’s now move into cloud and DevOps interview questions that test how well you handle deployment, monitoring, and production-readiness. 

Cloud & DevOps Data Engineer Interview Questions

Cloud and DevOps questions focus on how you deploy, monitor, and scale data infrastructure in production. Interviewers want to know if you can work with cloud platforms like AWS, GCP, or Azure, and automate workflows using CI/CD, containerization, and infrastructure-as-code. 

Here are a few common questions that test your readiness for real-world data operations.

19. What cloud platforms have you worked on (AWS/GCP/Azure)?

How to answer:

• List platforms you've used
• Mention services relevant to data engineering
• Highlight project use cases

Sample Answer:
I’ve worked mainly on AWS and GCP. In AWS, I’ve used S3 for storage, Glue for ETL, Redshift for warehousing, and Lambda for serverless processing. On GCP, I’ve used BigQuery, Cloud Storage, and Dataflow for building batch and streaming pipelines. I choose platforms based on project needs, data volume, and integration requirements.

20. Explain how you’d set up data storage in AWS for scalability.

How to answer:

• Start with S3 for raw and processed data
• Use Redshift or Athena for analytics
• Add lifecycle policies and partitioning

Sample Answer:
I’d use Amazon S3 to store raw, processed, and curated datasets in separate folders or buckets. For queryable storage, I’d use Redshift for structured analytics or Athena for serverless querying over S3. 

I’d apply partitioning (e.g., by date) and compression (e.g., Parquet) to optimize cost and speed. Lifecycle rules help manage storage costs by archiving or deleting old data automatically.

21. CI/CD pipelines in a data engineering project

How to answer:

• Define CI/CD in context of data workflows
• Mention tools like GitHub Actions, Jenkins, or Airflow
• Include testing, deployment, and rollback strategies

Sample Answer:
In data engineering, CI/CD ensures that data pipelines are versioned, tested, and deployed safely. I’ve used GitHub Actions to trigger tests when code is pushed, followed by deployment scripts that update DAGs in Airflow or code in Lambda functions. 

I include unit tests for data quality and rollback scripts to revert to previous states if needed. This setup reduces manual errors and keeps deployments smooth.

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Let’s now look at behavioral and scenario-based questions that reveal how you solve problems, work with teams, and learn from challenges.

Behavioral and Scenario-Based Questions

These questions focus on how you approach real-world problems, work within teams, and handle setbacks or pressure. Interviewers look for clear thinking, accountability, and communication, not just technical know-how. Here are some examples that test your mindset and problem-solving style.

22. Tell me about a time you optimized a slow pipeline.

How to answer:

• Briefly describe the original issue
• Explain the steps you took to investigate
• Share what you changed and the result

Sample Answer:
In a previous role, one batch pipeline was taking over six hours to process daily sales data. I reviewed the SQL queries and discovered multiple unnecessary joins and unindexed columns. I rewrote the queries, added proper indexing, and used partitioned data in S3. The processing time dropped to under one hour, improving data availability for downstream reports.

23. How do you collaborate with data scientists or analysts?

How to answer:

• Mention communication and shared goals
• Highlight how you support their needs
• Explain how you balance flexibility with structure

Sample Answer:
I work closely with data scientists and analysts to understand their data needs, whether for model training or business insights. I help create clean, reliable datasets and build pipelines that ensure consistent delivery. I also document data definitions clearly and keep communication open so they can focus on analysis while I ensure backend stability.

24. What’s your approach to documentation and versioning?

How to answer:

• Stress importance of clarity and reproducibility
• Mention tools used (Git, Confluence, etc.)
• Include version control for both code and data

Sample Answer:
I treat documentation as part of the development process. I maintain clear README files for each pipeline, use Git for versioning code, and log schema changes. For more complex workflows, I create architecture diagrams and update Confluence or internal wikis regularly. This ensures new team members can get up to speed quickly and audits are easy to handle.

You’ve seen the kinds of questions that come up. Technical, architectural, and behavioral. Now, let’s look at a few expert-backed tips to help you prepare smarter and stand out in your next data engineering interview.

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Expert Tips to Prepare for Data Engineer Interview Questions

Interviewers want more than tool names; they want to see how you think. A candidate at Spotify stood out by clearly explaining how they’d use Kafka, Spark, and S3 to track user activity in real time. Clear thinking beats buzzwords. 

Here are some smart tips to help you prepare like that.

Also Read: Data Engineer Salary in India 2025 [Average to Highest]

Knowing what to prepare is only half the battle. Having the right support makes a huge difference. Let’s look at how upGrad helps you build real skills, gain confidence, and get ready for your next data engineer interview with a solid plan.

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How upGrad Helps You Prepare for Data Engineer Interview Questions!

You’ve just seen the kind of questions data engineering interviews are built on: SQL challenges, pipeline design, cloud setup, big data tools, and real-world problem solving. These aren't just technical checks. They're meant to see how ready you are to build and maintain systems that actually work under pressure.

That’s where upGrad can make a real difference. Through hands-on courses built with real industry projects, you don’t just prep for interviews, you learn how to do the job. 

If you're looking to level up, check out some additional courses:

• Case Study using Tableau, Python and SQL
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• Analyzing Patterns in Data and Storytelling
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Need help figuring out what fits you best? Get free, personalized counseling or visit the nearest upGrad offline center for expert guidance and one-on-one support.

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Reference: 
https://365datascience.com/career-advice/data-engineer-job-outlook-2025/