If SQL is the foundation of Data Engineering, Python is the engine that powers modern data pipelines.

Almost every Data Engineering job today mentions Python alongside SQL, cloud platforms and data warehousing technologies. Whether you’re working with Snowflake, Databricks, Azure, AWS or Apache Airflow, Python has become an essential skill.

If you’re planning a career in Data Engineering, learning Python is one of the best investments you can make.

Why Do Data Engineers Use Python?

Data Engineers work with much more than databases.

They need to:

• Collect data from APIs
• Process CSV and JSON files
• Clean messy datasets
• Build ETL pipelines
• Automate repetitive tasks
• Move data between systems
• Schedule workflows
• Validate data quality

Python makes these tasks much faster and easier.

What Can You Build With Python?

Python is used throughout the entire data pipeline.

Examples include:

• ETL and ELT pipelines
• API integrations
• Data validation scripts
• File processing
• Cloud automation
• Database migrations
• Workflow automation
• Data transformation

Many companies use Python every day to process millions of records automatically.

Why Employers Love Python

Python is:

• Easy to learn
• Easy to read
• Cross-platform
• Supported by thousands of libraries
• Excellent for automation
• Ideal for cloud applications

Because of its flexibility, Python is widely used by:

• Data Engineers
• Data Scientists
• Machine Learning Engineers
• DevOps Engineers
• Cloud Engineers
• Software Developers

Learning Python opens the door to many different technology careers.

Python Libraries Every Data Engineer Should Know

One of Python’s biggest strengths is its ecosystem.

Some of the most useful libraries include:

Pandas

Used for:

• Reading CSV files
• Cleaning data
• Transforming datasets
• Aggregations
• Filtering

Pandas is often the first library Data Engineers learn.

Requests

Almost every company consumes APIs.

The Requests library allows you to:

• Call REST APIs
• Download JSON data
• Authenticate with services
• Automate integrations

API integration is a common Data Engineering task.

SQLAlchemy

SQLAlchemy allows Python applications to communicate with databases.

It supports:

• SQL Server
• PostgreSQL
• MySQL
• SQLite
• Oracle

It’s widely used in production applications.

PySpark

When companies process very large datasets, Python alone may not be enough.

PySpark combines Python with Apache Spark to process distributed data efficiently.

Large organisations frequently use PySpark together with Databricks.

NumPy

Although Data Engineers don’t use NumPy as heavily as Data Scientists, it’s useful for numerical operations and forms the foundation for many other Python libraries.

Python and SQL Work Together

A common misconception is that Python replaces SQL.

It doesn’t.

Instead, the two complement each other.

For example:

SQL retrieves data.

Python processes the results.

A typical workflow might be:

1. Query data using SQL.
2. Load results into Python.
3. Clean and transform the data.
4. Save it to Snowflake or another data warehouse.
5. Trigger automated reporting.

Modern Data Engineers use both technologies daily.

Python in Cloud Data Engineering

Cloud platforms integrate closely with Python.

Examples include:

Microsoft Azure

• Azure Functions
• Azure Data Factory
• Azure Storage
• Azure Synapse Analytics

Amazon Web Services

• AWS Lambda
• Glue
• S3
• Redshift

Google Cloud

• BigQuery
• Cloud Functions
• Cloud Storage

Understanding Python makes cloud development much easier.

Python and Airflow

Apache Airflow has become one of the most popular workflow orchestration tools.

Airflow workflows (DAGs) are written in Python.

This means Python knowledge is essential if you plan to automate modern data pipelines.

Python and Databricks

Databricks relies heavily on Python.

Many Data Engineers use Python notebooks to:

• Transform datasets
• Build ETL pipelines
• Process streaming data
• Train machine learning models

If you plan to work with Databricks, Python is a must-have skill.

Common Python Interview Questions

Data Engineering interviews often include questions such as:

• Explain lists and dictionaries.
• What are functions?
• What are generators?
• How do you handle exceptions?
• How do you read a CSV file?
• How do you call an API?
• Explain list comprehensions.
• What libraries have you used?

Interviewers are usually more interested in practical experience than obscure language features.

Build Python Projects

The best way to learn Python is by building real projects.

Ideas include:

• Import weather data from an API
• Process CSV sales reports
• Create an ETL pipeline
• Build a simple data warehouse
• Automate daily reports
• Load data into Snowflake
• Build an Airflow workflow

Publishing these projects on GitHub demonstrates practical experience.

Learning Roadmap

A practical learning path could be:

Step 1

Learn Python fundamentals.

• Variables
• Loops
• Functions
• Lists
• Dictionaries

Step 2

Learn file handling.

• CSV
• JSON
• Excel

Step 3

Learn APIs using Requests.

Step 4

Connect Python to SQL databases.

Step 5

Learn Pandas.

Step 6

Build ETL pipelines.

Step 7

Learn Airflow.

Step 8

Learn PySpark and Databricks.

Step 9

Deploy projects to Azure or AWS.

Is Python Worth Learning in 2026?

Absolutely.

Python continues to dominate Data Engineering because it’s simple, flexible and supported by almost every modern data platform.

Whether you’re building cloud pipelines, automating workflows or processing millions of records, Python remains one of the most valuable technical skills you can have.

Its popularity also means you’ll find excellent learning resources, strong community support and plenty of career opportunities.

Final Thoughts

Python has become an essential programming language for modern Data Engineers.

Combined with SQL, cloud platforms and data warehousing technologies, Python allows engineers to build reliable, scalable and automated data pipelines.

If you’re planning a career in Data Engineering, investing time in Python will significantly improve your technical skills and job opportunities.

Start with the fundamentals, build practical projects and gradually explore tools such as Pandas, Airflow, Snowflake and Databricks.

If you’re looking for your next Data Engineering opportunity, browse the latest Data Engineer jobs on SoftwareVacancy and discover companies hiring across Malta.

Frequently Asked Questions

Do Data Engineers need Python?

Yes. Python is one of the most requested programming languages for Data Engineering and appears in the majority of modern job descriptions.

Is Python better than SQL?

They serve different purposes. SQL retrieves and manages data, while Python automates workflows, processes data and integrates different systems. Most Data Engineers use both every day.

Which Python libraries should beginners learn?

Start with Pandas, Requests and SQLAlchemy. As you progress, learn Airflow, PySpark and cloud SDKs.

Do I need Python for Snowflake?

Basic SQL is enough to get started with Snowflake, but Python is commonly used for automation, ETL pipelines and integrating Snowflake with other systems.

Where can I find Data Engineering jobs requiring Python?

SoftwareVacancy regularly publishes Data Engineering opportunities requiring Python, SQL, Snowflake, Databricks, Airflow, Azure and AWS from employers hiring across Malta.