60 Data Engineer Interview Questions and Answers PDF 2025

A data engineer is a professional responsible for designing, building, and maintaining the systems that allow organizations to collect, store, and analyze data. They play a crucial role in the data analysis process by ensuring that data is accessible and usable for data analysts and data scientists.

Data Engineers are responsible for designing, developing, and maintaining data architecture such as databases and large-scale processing systems. They work with tools like SQL, Python, Apache Spark, and cloud platforms like AWS, Azure, or GCP. Their technical knowledge often spans across data modeling, ETL pipelines, API integrations, data warehousing, and real-time data processing.

Preparing well for a data engineering interview is crucial because it’s ahigh-demand, high-responsibility role that requires strong technical and problem-solving skills. Companies want engineers who not only understand the theory but can also handle real-world data infrastructure challenges.

Here, we are sharing many popular interview questions and answers that will assist you in preparing better for your next data analyst interview. Also, we will provide a PDF download, allowing you to prepare offline and revisit key concepts at your convenience.

Data Engineer Interview Questions and Answers for Freshers

1. What is the difference between structured, semi-structured, and unstructured data?

Answer:

• Structured Data: Organized in rows and columns (e.g., SQL tables).
• Semi-structured Data: Not in strict tabular format but has tags or markers (e.g., JSON, XML).
• Unstructured Data: No predefined structure (e.g., images, videos, text files).

Type	Format Examples	Storage Examples
Structured	CSV, SQL Tables	RDBMS (MySQL, PostgreSQL)
Semi-structured	JSON, XML	NoSQL (MongoDB, CouchDB)
Unstructured	Images, Videos	Object storage (S3, GCS)

2. Explain ETL and its stages.

Answer:

ETL stands for Extract, Transform, Load.

• Extract: Get data from various sources.
• Transform: Clean, format, and manipulate data.
• Load: Store the processed data in a database or warehouse.

3. What is the difference between OLTP and OLAP?

Answer:

Feature	OLTP (Online Transaction Processing)	OLAP (Online Analytical Processing)
Use Case	Day-to-day operations	Data analysis and reporting
Data Volume	Low to moderate	Large volumes
Query Type	Simple, read-write	Complex, read-only
Normalization	Highly normalized	De-normalized for fast querying

4. What is data normalization?

Answer:

Normalization is the process of organizing data in a database to reduce redundancy and improve integrity. It involves dividing large tables into smaller ones and defining relationships between them using keys.

Example:

-- Example: Breaking Customer data into two tables
CREATE TABLE Customer (
  CustomerID INT,
  Name VARCHAR(50)
);

CREATE TABLE CustomerAddress (
  CustomerID INT,
  Address VARCHAR(100)
);

5. What are the different types of joins in SQL?

Answer:

1. INNER JOIN – Returns matching rows from both tables.
2. LEFT JOIN – All rows from the left table + matching rows from the right.
3. RIGHT JOIN – All rows from the right table + matching from the left.
4. FULL JOIN – All rows from both tables.
5. CROSS JOIN – Cartesian product of both tables.

6. Write a SQL query to find duplicate values in a column.

Answer:

SELECT name, COUNT(*) 
FROM employees 
GROUP BY name 
HAVING COUNT(*) > 1;

7. What is a data pipeline?

Answer:

A data pipeline is a set of steps that move and transform data from source to destination systems. It includes data ingestion, transformation, and storage, often in an automated and repeatable flow.

8. What are some commonly used data ingestion tools?

Answer:

• Apache NiFi
• Apache Kafka
• AWS Kinesis
• Apache Flume
• Logstash

These tools help collect data from various sources and send it to storage or processing systems.

9. What is the difference between batch and stream processing?

Answer:

Feature	Batch Processing	Stream Processing
Data	Processes chunks at intervals	Processes data in real-time
Latency	High	Low
Use Case	Monthly reports	Real-time analytics, fraud detection

10. How is a data warehouse different from a database?

Answer:

• Database: Optimized for CRUD operations (create, read, update, delete).
• Data Warehouse: Optimized for analytical queries and historical data.

11. What is partitioning in a database or data warehouse?

Answer:

Partitioning is dividing a table into smaller, manageable parts called partitions to improve performance and manageability. Example: partitioning a sales table by month or region.

12. What is Apache Hadoop and its core components?

Answer:

Hadoop is a framework for distributed storage and processing of big data.

Core components:

• HDFS (Hadoop Distributed File System) – Storage layer.
• MapReduce – Processing engine.
• YARN – Resource manager.

13. What is Apache Spark and how is it different from MapReduce?

Answer:

Apache Spark is an in-memory distributed processing engine faster than MapReduce.

Feature	MapReduce	Spark
Processing	Disk-based	In-memory
Speed	Slower	Faster
Ease of Use	Complex (Java)	Simple (Python, Scala)

14. Explain the role of Kafka in data engineering.

Answer:

Apache Kafka is a distributed event streaming platform used to build real-time data pipelines. It enables high-throughput, low-latency communication between data-producing and data-consuming systems.

15. What is schema evolution in data lakes?

Answer:

Schema evolution allows changing the schema (adding/removing columns) over time without breaking existing data pipelines. Data formats like Avro and Parquet support schema evolution.

16. What is a slowly changing dimension (SCD) in data warehousing?

Answer:

SCD refers to how data changes are handled in dimension tables over time.

Types:

• Type 1: Overwrite old data.
• Type 2: Store historical changes as new rows.
• Type 3: Track limited history with additional columns.

17. What is the CAP theorem?

Answer:

CAP theorem states that a distributed system can only guarantee two out of three:

• Consistency
• Availability
• Partition Tolerance

18. What is the difference between star schema and snowflake schema?

Answer:

• Star Schema: Central fact table linked directly to dimension tables.
• Snowflake Schema: Dimension tables are normalized into multiple related tables.

Feature	Star Schema	Snowflake Schema
Simplicity	Simple	Complex
Joins	Fewer	More
Query Speed	Faster	Slower

19. How do you handle data quality issues?

Answer:

Steps to ensure data quality:

• Data validation rules
• Null checks
• Duplicate removal
• Data profiling tools
• Implementing tests in pipelines

20. Explain a scenario where you built or worked on a data pipeline.

Answer:

Example answer:

“I worked on a data pipeline that ingested user activity logs from a web application using Kafka. These logs were processed using Spark for sessionization, and the results were stored in AWS Redshift for reporting. I also added monitoring alerts using CloudWatch to ensure the pipeline stayed healthy.”

Also Check: Data Analyst Interview Questions and Answers

Data Engineer Interview Questions and Answers for Experience

1. What are the main differences between a data lake and a data warehouse?

Answer:

Feature	Data Lake	Data Warehouse
Data Type	Raw, unstructured, semi-structured	Structured
Storage Cost	Low	High
Processing	Schema-on-read	Schema-on-write
Use Cases	Big data, ML	BI, Reporting

2. How do you choose between batch and real-time processing in a pipeline?

Answer:

• Use batch processing when data can be processed in scheduled intervals and latency is acceptable.
• Use real-time processing when low-latency or event-driven systems are required, such as fraud detection or live analytics.

3. Explain how partitioning improves performance in big data systems.

Answer:

Partitioning divides data into segments based on specific fields (e.g., date, region). It reduces the amount of data scanned during queries, improves parallel processing, and optimizes read/write performance.

4. What is data shuffling in Spark, and how do you avoid it?

Answer:

Data shuffling is the movement of data across nodes to perform operations like groupBy, join, or repartition. It’s expensive and can cause performance issues.

To reduce shuffling:

• Use partitioning strategies
• Broadcast smaller tables during joins
• Avoid wide transformations when possible

5. What is the role of metadata in data engineering?

Answer:

Metadata describes the structure, source, quality, and lineage of data. It is crucial for:

• Data governance
• Data cataloging
• Query optimization
• Pipeline debugging

6. How do you handle schema drift in streaming data pipelines?

Answer:

• Use schema-aware formats like Avro or Parquet
• Implement schema registries (e.g., Confluent Schema Registry)
• Create alerts and fallbacks for schema mismatches
• Maintain backward-compatible transformations

7. What is data lineage and why is it important?

Answer:

Data lineage tracks the origin and movement of data through systems. It’s important for:

• Auditing and compliance
• Debugging errors
• Impact analysis
• Understanding data flow

8. What are some best practices for writing scalable ETL pipelines?

Answer:

• Modularize pipeline code
• Use idempotent operations
• Handle errors and retries
• Minimize data movement
• Use parallel and distributed processing
• Monitor and log metrics

9. How do you optimize performance in a Spark job?

Answer:

• Use caching for reused datasets
• Avoid shuffles by filtering early
• Tune executor memory and cores
• Use broadcast joins for small datasets
• Optimize file sizes and partitions

10. What are slowly changing dimensions and how do you handle them in ETL?

Answer:

SCDs represent changes in dimension data over time.

Handling:

• Type 1: Overwrite the old data
• Type 2: Add a new row for each change
• Type 3: Add a new column to track changes

Example (SCD Type 2):

-- Add new row with current flag
INSERT INTO dim_customer (...)
VALUES (..., 'Y', CURRENT_DATE);

-- Set current flag 'N' for previous row
UPDATE dim_customer
SET current_flag = 'N'
WHERE customer_id = ...;

11. How would you monitor and alert for failures in a data pipeline?

Answer:

• Use logging frameworks (e.g., Log4j, Fluentd)
• Integrate with monitoring tools (e.g., Prometheus, Datadog, CloudWatch)
• Implement retry mechanisms
• Create alerts for failure metrics or missed schedules

12. What is the difference between repartition and coalesce in Spark?

Answer:

• repartition(n): Increases or decreases the number of partitions by shuffling data.

• coalesce(n): Reduces the number of partitions by merging without full shuffle.

Use coalesce when reducing partitions to improve performance.

13. How do you design a fault-tolerant data pipeline?

Answer:

• Use checkpointing (e.g., in Spark Streaming)
• Retry on transient failures
• Use atomic writes or transactions
• Implement error queues or dead letter queues
• Maintain idempotent transformations

14. How do you handle late-arriving data in stream processing?

Answer:

• Use windowing functions with watermarking (in Spark or Flink)
• Store events in buffer for a defined delay
• Perform data reconciliation with batch reprocessing if needed

15. What is a distributed file system, and why is it important in big data?

Answer:

A distributed file system (e.g., HDFS) stores data across multiple nodes for scalability and fault-tolerance. It’s crucial for big data to support large volumes of data and parallel processing.

16. Explain the concept of backpressure in streaming systems.

Answer:

Backpressure occurs when the rate of incoming data exceeds the system’s processing capacity, leading to buffering and potential crashes.

Solutions:

• Throttle data sources
• Scale up processing
• Use buffering mechanisms

17. What are common challenges in data deduplication?

Answer:

• Identifying duplicates in massive datasets
• Handling slightly mismatched data (fuzzy matching)
• Avoiding false positives/negatives
• Managing performance and memory usage

18. How do you ensure data consistency across distributed systems?

Answer:

• Use transaction mechanisms if available
• Implement idempotent operations
• Use exactly-once processing techniques
• Employ data validation and reconciliation checks

19. How do you secure data in pipelines and storage?

Answer:

• Encrypt data at rest and in transit
• Use IAM and RBAC for access control
• Mask sensitive fields
• Log access and changes for auditing
• Use VPCs and firewalls to restrict access

20. Describe a complex data engineering project you’ve worked on and the challenges you faced.

Answer:

Example:

“I built a data lake ingestion pipeline for a fintech app using Kafka, Spark Streaming, and S3. The challenge was ensuring near real-time performance while handling spikes in traffic. I implemented dynamic partitioning, checkpointing, and autoscaling of Spark jobs. We also added schema evolution support and used AWS Glue for cataloging.”

Azure Data Engineer Interview Questions and Answers

1. What is the difference between Azure Data Factory and Azure Synapse Pipelines?

Answer:

Both are used for data integration and orchestration, but they differ in their scope and target users.

Feature	Azure Data Factory	Azure Synapse Pipelines
Purpose	General-purpose data integration	Built into Synapse for analytics
Integration Scope	Broader (supports many services)	Limited to Synapse environment
UI	ADF Studio	Synapse Studio
Use Case	Standalone data workflows	Integrated with Synapse analytics

2. How does PolyBase work in Azure Synapse Analytics?

Answer:

PolyBase enables querying external data directly from sources like Azure Blob Storage or Data Lake without importing it into Synapse. It uses external tables to reference the data.

Example:

CREATE EXTERNAL TABLE sales_ext (
  OrderID INT, Amount FLOAT
)
WITH (
  LOCATION = 'sales/',
  DATA_SOURCE = my_external_ds,
  FILE_FORMAT = my_file_format
);

Benefits:

• Saves storage
• Useful for large files
• Supports federated queries

3. What are Integration Runtimes in Azure Data Factory?

Answer:

Integration Runtime (IR) is the compute infrastructure used by ADF to:

• Move data between sources
• Transform data
• Dispatch activities

Types of IR:

1. Azure IR – For cloud data movement and transformation
2. Self-hosted IR – For on-premise or private network access
3. Azure-SSIS IR – For running SSIS packages in Azure

4. How would you implement incremental data loading in Azure Data Factory?

Answer:

Incremental loading avoids reloading full datasets and only loads new or changed data.

Approaches:

• Use a Watermark column (e.g., LastModifiedDate)
• Store the last loaded value in a variable/table
• Use it in a query to fetch only new rows

Example in query:

SELECT * FROM sales
WHERE LastUpdated > '@{variables('LastLoadedDate')}'

5. What is Delta Lake and how does it work with Azure?

Answer:

Delta Lake is an open-source storage layer that brings ACID transactions and schema enforcement to big data workloads. It is commonly used with Azure Databricks.

Benefits:

• Time travel for data versioning
• Scalable metadata handling
• Supports CDC and schema evolution

Example with PySpark:

df.write.format("delta").save("/mnt/datalake/sales/")

AWS Engineer Interview Questions and Answers

1. What is Amazon S3 and how is it used in data engineering?

Answer:

Amazon S3 (Simple Storage Service) is a scalable object storage service. It is commonly used by data engineers to:

• Store raw and processed data
• Serve as input/output storage for ETL pipelines
• Integrate with AWS services like Glue, EMR, and Redshift
• Manage data with features like versioning, lifecycle policies, and access control

Example Use Case: Store CSV files uploaded from a website and use AWS Glue to transform and load them into Redshift.

2. What is AWS Glue and how does it help in ETL?

Answer:

AWS Glue is a serverless data integration service that automates the discovery, cataloging, transformation, and movement of data.

Key Features:

• Crawlers to discover metadata and create tables
• Glue Studio to design ETL workflows
• Supports both visual and code-based development (Python/Scala)
• Integrates with S3, Redshift, RDS, and more

Example: You can use a Glue Job to read data from S3, apply transformations in PySpark, and write the output back to S3 or Redshift.

3. How do you implement data partitioning in Amazon Redshift?

Answer:

Redshift does not support traditional partitioning like other databases, but performance can be optimized using:

• DISTKEY: Controls how data is distributed across nodes
• SORTKEY: Determines how data is sorted on disk for faster queries
• Column encoding: Reduces storage size

Best Practice: Use time-based sort keys and appropriate distribution style for large fact tables.

4. What is Amazon Kinesis and how is it used in real-time data engineering?

Answer:

Amazon Kinesis is a real-time streaming platform used to collect, process, and analyze data streams.

Components:

• Kinesis Data Streams: Collect streaming data
• Kinesis Data Firehose: Load streaming data to destinations like S3 or Redshift
• Kinesis Data Analytics: Run SQL queries on streams

Use Case: Ingest clickstream data from a web app and deliver it in near real-time to S3 and Redshift for analytics.

5. How do you secure data in AWS data engineering workflows?

Answer:

To secure data:

• Encrypt data at rest using KMS (S3, Redshift, RDS support it)
• Encrypt data in transit using SSL/TLS
• IAM roles and policies to control access
• Use VPCs, private subnets, and security groups for network-level security
• Enable CloudTrail for audit logs

Example: Attach an IAM role to a Glue job that allows read/write access only to a specific S3 bucket.

Python Engineer Interview Questions and Answers

1. What are Python decorators and how do they work?

Answer:

A decorator in Python is a function that wraps another function to extend its behavior without modifying its source code. Decorators are commonly used for logging, authentication, and caching.

Example:

def my_decorator(func):
    def wrapper():
        print("Before function call")
        func()
        print("After function call")
    return wrapper

@my_decorator
def say_hello():
    print("Hello")

say_hello()

Output:

Before function call  
Hello  
After function call

2. How is memory managed in Python?

Answer:

Python uses automatic memory management through the following:

• Reference counting: Every object has a reference count that tracks how many references point to it.
• Garbage collection: A cyclic garbage collector detects and removes circular references.
• Private heaps: All Python objects and data structures are stored in a private heap.

Tools for inspection:

• gc module (to control garbage collection)
• sys.getsizeof() to check memory usage

3. Explain list comprehension with an example.

Answer:

List comprehension provides a concise way to create lists.

Example:

# Squares of even numbers from 1 to 10
squares = [x**2 for x in range(1, 11) if x % 2 == 0]
print(squares)

Output:

[4, 16, 36, 64, 100]

It is more readable and faster than traditional loops for creating lists.

4. What is the difference between shallow copy and deep copy in Python?

Answer:

• Shallow copy: Creates a new object, but references the same nested objects.
• Deep copy: Creates a new object and recursively copies all nested objects.

Example:

import copy

original = [[1, 2], [3, 4]]
shallow = copy.copy(original)
deep = copy.deepcopy(original)

Modifying original[0][0] will also affect shallow, but not deep.

5. How do you handle exceptions in Python?

Answer:

Python uses try-except blocks to handle exceptions gracefully.

Example:

try:
    result = 10 / 0
except ZeroDivisionError as e:
    print(f"Error: {e}")
finally:
    print("Always executes")

Output:

Error: division by zero  
Always executes

You can also use multiple except blocks or catch all exceptions using except Exception as e.

Need more questions? Check this: Python Interview Questions and Answers

SQL Engineer Interview Questions and Answers

1. What is the difference between “WHERE”and “HAVING” clauses in SQL?

Answer:

• WHERE is used to filter rows before grouping.
• HAVING is used to filter groups after aggregation.

Example:

-- Get departments with more than 5 employees
SELECT department_id, COUNT(*) AS total_employees
FROM employees
WHERE status = 'active'
GROUP BY department_id
HAVING COUNT(*) > 5;

2. How do you find the second highest salary from a table?

Answer:

Using LIMIT and OFFSET (MySQL/PostgreSQL):

SELECT DISTINCT salary
FROM employees
ORDER BY salary DESC
LIMIT 1 OFFSET 1;

Using a subquery (works on most SQL platforms):

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

3. What are indexes and how do they improve performance?

Answer:

An index is a database object that speeds up data retrieval. It works like a lookup for fast access to rows in a table.

Types:

• Primary index
• Unique index
• Composite index
• Full-text index

Example:

CREATE INDEX idx_lastname ON employees(last_name);

Downside: Indexes speed up reads but slow down inserts and updates.

Explain the difference between INNER JOIN, LEFT JOIN, and FULL JOIN.

Answer:

Join Type	Description
INNER JOIN	Returns only matching rows from both tables
LEFT JOIN	All rows from left + matching from right table
FULL JOIN	All rows from both tables

Example:

SELECT a.name, b.department
FROM employees a
LEFT JOIN departments b ON a.dept_id = b.id;

5. What are window functions in SQL? Give an example.

Answer:

Window functions perform calculations across a set of rows related to the current row without collapsing rows like GROUP BY.

Example:

SELECT name, department, salary,
       RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS dept_rank
FROM employees;

This ranks employees by salary within each department.

FAQs: Data Engineer Interview

Conclusion

In this guide, we’ve shared important Data Engineer interview questions and answers for both freshers and experienced candidates. The questions cover core topics like SQL, Python, Azure, AWS, and real-world data engineering tasks.

We’ve included a mix of beginner to advanced-level questions to help you prepare confidently. You can also download the PDF to revise offline.

We hope this guide helps you get ready and perform well in your upcoming data engineer interview. Good luck!