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Hire Vetted Apache Airflow Engineers
Find engineers who design and operate Airflow-orchestrated ML pipelines and data workflows — maintainable DAGs, robust retry logic, and cloud-native deployments on Kubernetes or Astronomer.
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Adithya Prahasith
Moises
Robin
Andrew
Vipin
Alamdar
Amine
Krishna Chaitanya
Michelle
Hiring guide
Why companies hire Airflow experts
Apache Airflow is the most widely used ML pipeline orchestration tool. Engineers who work with Airflow professionally design maintainable DAGs, implement robust retry and monitoring logic, and integrate Airflow with cloud data infrastructure.
Popular projects
- Automate your ML model retraining on a schedule or data trigger
- Orchestrate multi-step data ingestion, transformation, and training pipelines
- Coordinate workflows across Spark, dbt, and ML systems reliably
- Replace fragile cron jobs with monitored, retryable Airflow DAGs
What to look for
- Production Airflow DAG design experience (modularity, testing)
- Knowledge of Airflow operators, sensors, and hooks
- Experience with Airflow on Kubernetes or Astronomer
Frequently asked questions
When should I choose Airflow over Prefect or Dagster?
Airflow is the most mature and widely adopted — with the largest ecosystem and community. Prefect and Dagster offer a better developer experience for new projects. If your team has existing Airflow infrastructure, hiring Airflow engineers makes sense.
Can Airflow orchestrate ML model retraining automatically?
Yes. Airflow engineers design DAGs that trigger model retraining based on schedules, data availability, or drift detection events — ensuring your models stay fresh without manual intervention.
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