Senior Python Engineer – Airflow and Workflow Orchestration

Job Description

A leading organization is seeking a Senior Python Engineer with expertise in workflow orchestration, Apache Airflow, and the Astronomer platform. This position will focus on modernizing and enhancing enterprise workflow management capabilities, including a major migration initiative and ongoing optimization of production data pipelines.

The ideal candidate brings strong Python development skills, hands-on experience with Airflow platform upgrades, and a deep understanding of scalable workflow orchestration in cloud environments.

Responsibilities

• Lead the migration of Apache Airflow environments from version 2 to version 3 while ensuring stability and operational continuity.
• Design, develop, and maintain production-grade workflow orchestration solutions using Apache Airflow.
• Create and support Python-based automation, scheduling, and data workflow processes.
• Improve the performance, reliability, and scalability of workflow execution and pipeline operations.
• Troubleshoot orchestration issues, resolve scheduling challenges, and optimize system performance.
• Collaborate with data engineering and platform teams to establish best practices for orchestration, deployment, monitoring, and operational support.
• Implement maintainable and efficient workflow architectures that support business and technical requirements.
• Contribute to continuous improvements across workflow management and data pipeline operations.

Required Experience and Skills

• 5–8 years of professional experience in software engineering, data engineering, or workflow orchestration environments.
• Expert-level Python development experience.
• Hands-on experience migrating and supporting Apache Airflow version 2 to version 3.
• Strong experience working with the Astronomer platform.
• Deep understanding of Airflow DAG development, dependency management, scheduling, and operational best practices.
• Experience building, supporting, and optimizing production data pipelines.
• Experience working with cloud-based platforms and distributed data processing environments.
• Strong troubleshooting and problem-solving skills with a focus on reliability, scalability, and operational excellence.
• Ability to collaborate effectively with engineering and platform teams in a production environment.

FAQ

1. What does a Senior Python Engineer specializing in Airflow and workflow orchestration do?

A Senior Python Engineer in this area designs, develops, and maintains automated workflows for data and application processes. The role combines Python development with Apache Airflow to build reliable, scalable, and observable pipelines that coordinate tasks across databases, cloud services, APIs, and data platforms.

2. How is Apache Airflow used in this role?

Apache Airflow is used to define, schedule, monitor, and manage workflows as directed acyclic graphs (DAGs). Senior engineers design reusable and fault-tolerant DAGs, manage task dependencies, configure retries and alerts, and improve workflow performance and operational reliability.

3. What type of Python development is expected?

Python is used to develop workflow logic, custom operators, integrations, automation scripts, validation processes, and supporting services. Senior engineers are expected to write maintainable code and apply software engineering practices such as testing, modular design, logging, and error handling.

4. What kinds of workflows are typically orchestrated?

Workflows may include ETL and ELT pipelines, data ingestion, transformation jobs, API integrations, batch processing, machine learning workflows, database operations, and cloud infrastructure tasks. The exact scope depends on the organization’s data and application environment.

5. How are workflow reliability and fault tolerance maintained?

Engineers configure dependency management, retries, timeouts, failure handling, alerting, and monitoring within Airflow workflows. They also design tasks to be idempotent where appropriate and use logging and observability practices to simplify troubleshooting.

6. What cloud and data technologies commonly integrate with Airflow?

Airflow can orchestrate workloads across platforms such as AWS, Azure, and Google Cloud, as well as systems like Snowflake, Databricks, PostgreSQL, Kubernetes, and cloud storage services. Experience integrating APIs and external data platforms is also useful.