If you’ve looked at Data Engineering job descriptions recently, you’ve probably noticed one tool appearing repeatedly: Apache Airflow.
Whether you’re applying for roles in fintech, iGaming, SaaS or enterprise software, Airflow has become one of the most requested skills alongside SQL, Python, Snowflake and cloud platforms.
But why has Airflow become so popular?
The answer is simple: modern companies need a reliable way to automate and manage complex data pipelines.
What Is Apache Airflow?
Apache Airflow is an open-source workflow orchestration platform.
Instead of manually running scripts every day, Airflow allows Data Engineers to automate workflows and schedule tasks.
For example, a company may need to:
- Download data from an API
- Clean the data
- Transform it
- Load it into Snowflake
- Refresh dashboards
- Send notifications if something fails
Airflow can automate this entire process.
What Is Workflow Orchestration?
Imagine your company receives sales data every morning.
The process might look like this:
- Download CSV files.
- Validate the data.
- Remove duplicates.
- Transform the data.
- Load it into the data warehouse.
- Refresh Power BI dashboards.
- Notify the team if everything succeeds.
Without orchestration, someone would need to trigger each step manually.
Airflow automates the entire workflow.
Why Companies Use Airflow
Modern businesses process huge amounts of data every day.
Running hundreds of scripts manually simply isn’t practical.
Airflow helps organisations:
- Automate pipelines
- Schedule workflows
- Monitor jobs
- Retry failed tasks
- Manage dependencies
- Improve reliability
- Reduce manual work
This saves both time and money.
Python-Based Workflows
One reason Airflow is so popular is that workflows are written in Python.
Instead of learning a completely new language, Data Engineers use familiar Python code to define workflows.
Example:
with DAG("daily_sales_pipeline"):
extract_data()
transform_data()
load_to_snowflake()
If you already know Python, learning Airflow becomes much easier.
What Is a DAG?
One of Airflow’s core concepts is the DAG.
DAG stands for:
Directed Acyclic Graph
Although the name sounds complicated, it simply represents a workflow.
For example:
Extract Data
↓
Clean Data
↓
Transform Data
↓
Load to Snowflake
↓
Refresh Dashboard
Airflow executes each task in the correct order.
Airflow Integrates With Everything
One reason employers like Airflow is its flexibility.
It integrates with:
- Snowflake
- Databricks
- SQL Server
- PostgreSQL
- MySQL
- Azure
- AWS
- Google Cloud
- Kubernetes
- Docker
- Power BI
- Tableau
- Slack
- Microsoft Teams
This allows companies to automate entire data platforms.
Scheduling Pipelines
Airflow makes scheduling incredibly simple.
Examples include:
- Every hour
- Every night
- Every Monday
- Every month
- Every five minutes
Once configured, pipelines run automatically without human intervention.
Monitoring Pipelines
Running a pipeline isn’t enough.
Companies also need to know when something goes wrong.
Airflow provides:
- Workflow history
- Logs
- Task status
- Error messages
- Retry functionality
- Email notifications
This visibility makes troubleshooting much easier.
Common Airflow Use Cases
Airflow is commonly used for:
- ETL pipelines
- ELT pipelines
- API integrations
- Database synchronisation
- Machine learning workflows
- Data warehouse loading
- Report generation
- Cloud automation
Almost any repeatable data process can be automated with Airflow.
Airflow and Cloud Platforms
Airflow works well with modern cloud services.
Examples include:
Microsoft Azure
- Azure Data Lake
- Azure SQL
- Azure Blob Storage
- Azure Synapse
Amazon Web Services
- S3
- Redshift
- Glue
- Lambda
Google Cloud
- BigQuery
- Cloud Storage
- Dataflow
This flexibility makes Airflow attractive to companies regardless of which cloud platform they use.
Airflow vs Azure Data Factory
Many beginners wonder whether they should learn Airflow or Azure Data Factory.
Azure Data Factory is an excellent low-code orchestration tool for Microsoft environments.
Airflow offers:
- Greater flexibility
- Python support
- More customisation
- Open-source ecosystem
- Multi-cloud support
Many organisations actually use both together.
Airflow vs Prefect
Prefect has become increasingly popular in recent years.
Compared with Airflow:
Airflow offers:
- Larger community
- More integrations
- Enterprise adoption
- Extensive documentation
Prefect offers:
- Simpler development experience
- Modern architecture
- Easier local testing
Airflow still remains the most widely recognised workflow orchestration platform.
Skills Employers Want
Data Engineering job descriptions commonly combine Airflow with:
- Python
- SQL
- Snowflake
- Databricks
- Azure
- AWS
- Docker
- Kubernetes
- Git
- Terraform
Learning Airflow alongside these technologies significantly improves your employability.
Common Airflow Interview Questions
Interviewers may ask:
- What is Airflow?
- What is a DAG?
- What are Operators?
- What are Tasks?
- How does scheduling work?
- How do retries work?
- How do you monitor pipelines?
- How do you trigger workflows?
Practical experience is generally more valuable than memorising definitions.
Learning Roadmap
A practical learning path might look like this:
- Learn SQL.
- Learn Python.
- Build ETL pipelines.
- Install Airflow locally.
- Create your first DAG.
- Schedule workflows.
- Connect to Snowflake or PostgreSQL.
- Deploy Airflow using Docker.
- Learn cloud integrations.
- Build production-style projects.
Is Airflow Worth Learning in 2026?
Absolutely.
Apache Airflow has become one of the most widely adopted workflow orchestration platforms in Data Engineering.
As companies continue investing in cloud data platforms and automation, Airflow remains a highly valuable skill for Data Engineers.
Learning Airflow alongside SQL, Python, Snowflake and cloud technologies will significantly improve your career opportunities.
Final Thoughts
Airflow has transformed how organisations manage data pipelines.
Instead of relying on manual scripts and scheduled tasks, companies can build reliable, scalable and fully automated workflows.
Whether you’re building ETL pipelines, loading data into Snowflake or orchestrating machine learning workflows, Airflow is likely to remain one of the most valuable tools in modern Data Engineering.
If you’re planning a career in data, investing time in Airflow is well worth it.
Browse the latest Data Engineering jobs on SoftwareVacancy and discover employers looking for Airflow skills across Malta.
Frequently Asked Questions
Is Airflow difficult to learn?
If you’re comfortable with Python and basic Data Engineering concepts, Airflow is relatively straightforward to learn.
Do I need Python for Airflow?
Yes. Airflow workflows are defined in Python, making Python one of the most important prerequisites.
Is Airflow better than Azure Data Factory?
They serve different purposes. Airflow offers greater flexibility and customisation, while Azure Data Factory provides a low-code experience that integrates well with Microsoft services.
Do companies still use Airflow?
Yes. Apache Airflow remains one of the most widely adopted workflow orchestration tools across data engineering teams worldwide.
Where can I find Data Engineering jobs requiring Airflow?
SoftwareVacancy regularly publishes Data Engineering opportunities requiring Airflow, SQL, Python, Snowflake, Databricks and cloud technologies from employers hiring across Malta.