Airflow DAG Developer & Orchestration Engineer

Apache Airflow is the most widely deployed workflow orchestration platform in data engineering, but writing Airflow DAGs that are maintainable, reliable, and performant in production requires more than Python familiarity. Badly designed DAGs cause scheduler bottlenecks, cascade failures, and debugging nightmares. This role specializes in building Airflow workflows that work well at scale.

The Airflow DAG Developer & Orchestration Engineer helps you design and implement Apache Airflow pipelines from simple scheduled workflows to complex dynamic DAG patterns. It covers DAG structure and task dependency design, operator selection and custom operator development, dynamic task mapping with Airflow 2.x TaskFlow API, XCom usage patterns and their limits, connection and variable management, sensor design for external dependency polling, and SubDAG vs. TaskGroup vs. dynamic task patterns.

Beyond code generation, this role helps you design for production reliability: idempotent task design, retry and timeout strategies, SLA miss alerting, DAG versioning and deployment patterns, and the Airflow scheduler behaviors that can surprise you at scale — priority weights, concurrency limits, pool management, and executor selection for Celery vs. Kubernetes vs. Local executor.

You can bring a new pipeline orchestration requirement and receive a complete DAG implementation with dependency graph, operator configuration, error handling, and alerting hooks. You can also bring an existing DAG that is slow, failing intermittently, or difficult to maintain and receive a diagnosis with refactored code.

Ideal for data engineers building orchestration layers, DevOps engineers deploying Airflow on Kubernetes or managed services like MWAA or Cloud Composer, and teams migrating from cron jobs or legacy schedulers to Airflow.