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Remote Apache Airflow Jobs

Browse remote Airflow positions at companies running production ML pipelines — DAG engineering, workflow orchestration, and MLOps automation roles.

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Posted 9 months ago

Applications closed

MLOps Engineer — Model Deployment & Monitoring

Synaptic Labs

Remote (EU timezone preferred)

senior
freelance
English
docker kubernetes mlflow airflow aws +2 more
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Role guide

What to expect in remote Airflow jobs

Apache Airflow is the most widely deployed ML pipeline orchestration tool. Remote Airflow jobs involve designing maintainable DAGs, integrating Airflow with cloud data infrastructure, and ensuring ML training pipelines run reliably. Engineers with Airflow experience plus ML system knowledge are in consistent demand.

What you'll work on

  • Build Airflow DAGs that automate ML model retraining on schedule or data trigger
  • Orchestrate multi-step pipelines across Spark, dbt, and ML systems
  • Monitor and alert on pipeline failures in production data workflows
  • Migrate fragile cron jobs to reliable, retryable Airflow workflows

What companies look for

  • Production DAG design — modularity, testing, failure handling
  • Airflow operator, sensor, and hook development experience
  • Knowledge of Airflow deployment on Kubernetes or Astronomer

Frequently asked questions

What Airflow roles are available remotely?

Primarily data engineering and MLOps roles where Airflow is part of the orchestration stack. Pure "Airflow engineer" titles are rarer — most roles combine Airflow with Python, cloud platforms, and data tools like dbt or Spark.

Do remote Airflow jobs require Kubernetes experience?

Increasingly yes. KubernetesExecutor and Astronomer deployments are common at scale. Smaller companies may use CeleryExecutor or LocalExecutor — check individual job listings for infrastructure specifics.

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