Showcase Your Apache Airflow Skills with a GitHub README Badge

Use this markdown in your README:

![Apache Airflow](https://img.shields.io/badge/Apache%20Airflow-017CEE?style=for-the-badge&logo=apacheairflow&logoColor=white)

The #017CEE is Apache Airflow's brand blue from their official guidelines. The apacheairflow logo identifier renders Apache Airflow's logo from Simple Icons. This blue badge pairs well with Python (Airflow's primary language), Kubernetes, and other data infrastructure tools in a data engineering-focused profile.

Airflow's DAG-based architecture has significant depth. Specify your experience level:

• DAG authoring: Python DAGs with operators (PythonOperator, BashOperator, SQL operators)
• Scheduling: Cron expressions, data interval concepts, backfill strategies
• Dependencies: Task dependencies, branching (BranchPythonOperator), conditional execution
• Connections and variables: Configuring external connections, managing secrets
• Executors: LocalExecutor vs. CeleryExecutor vs. KubernetesExecutor for scalability
• TaskFlow API: Modern @task decorator pattern for cleaner DAG code
• Monitoring: SLA miss detection, alerting, DAG performance optimization

Using the TaskFlow API with typed Python functions rather than classic operator patterns signals modern Airflow authoring practices that teams migrating to Airflow 2.x specifically want.

Airflow orchestrates multiple data tools in a pipeline:

• Classic ELT stack: Airflow + Python + PostgreSQL/Snowflake + dbt
• Big data stack: Airflow + Spark + HDFS/S3 + Hive
• ML pipeline: Airflow + Python + MLflow + PostgreSQL + Docker
• Cloud data stack: Airflow + BigQuery/Redshift + dbt + Python

Airflow and dbt are a powerful combination — Airflow handles scheduling and orchestration, dbt handles SQL transformations. This tandem appears in most modern data warehouse architectures and is worth listing together if you use both. Many data engineering job postings now specifically list this combination as a preferred skill.

Airflow DAGs are Python files, so your language stats will show Python as your primary language, which is accurate for data engineering work. The presence of a dags/ directory with well-organized DAG files and a tests/ directory with DAG validation tests in your repositories signals data pipeline engineering discipline.

For pinned repositories, a production-style Airflow setup with Docker Compose, example DAGs covering common patterns (file sensor, SQL operator, Python operator, branch operator), and documentation describing the pipeline architecture is a strong data engineering portfolio piece. DAG testing — verifying DAG import time, no circular dependencies, correct task structure — is the data engineering equivalent of unit tests and demonstrates the same engineering maturity.