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Apache Airflow Python Jobs (NOW HIRING)

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$125 - $150/hr

You'll provide Apache Airflow expertise directly to customers to help them make the best possible ... Data Engineering background * 4 years of experience with Python * 1 year of experience in Airflow ...

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$125 - $150/hr

You'll provide Apache Airflow expertise directly to customers to help them make the best possible ... Data Engineering background * 4 years of experience with Python * 1 year of experience in Airflow ...

Astronomer

$125 - $150/hr

You'll provide Apache Airflow expertise directly to customers to help them make the best possible ... Data Engineering background * 4 years of experience with Python * 1 year of experience in Airflow ...

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NY · On-site

$125 - $150/hr

You'll provide Apache Airflow expertise directly to customers to help them make the best possible ... Data Engineering background * 4 years of experience with Python * 1 year of experience in Airflow ...

Polluxa, Inc.

Engineer - Technology AI ML

Dallas, TX · On-site

$125 - $150/hr

Proficiency in Python and Node.js programming languages. * Experience with AWS and data processing frameworks like Apache Airflow and Apache Spark. * Strong understanding of machine learning concepts ...

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Winaxis

Lead Python Developer

New York, NY · On-site

$153K - $188K/yr

Lead Python Developer - Lead the design, development, and deployment of enterprise-grade ... with Apache Airflow; integrate AI/ML models for advanced business insights. - Ensure solutions ...

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How much do apache airflow python jobs pay per year?

As of Sep 9, 2026, the average yearly pay for apache airflow python in the United States is $141,976.00, according to ZipRecruiter salary data. Most workers in this role earn between $121,500.00 and $163,500.00 per year, depending on experience, location, and employer.

What is an Apache Airflow Python developer?

An Apache Airflow Python developer is a software engineer who specializes in building, deploying, and maintaining data workflows using Apache Airflow, primarily utilizing the Python programming language. They create Directed Acyclic Graphs (DAGs) in Python to automate and orchestrate complex data pipelines. Their responsibilities often include integrating various data sources, managing dependencies, monitoring task execution, and optimizing performance. This role is crucial in data engineering and ETL (Extract, Transform, Load) processes, ensuring reliable and scalable workflow automation.

What are the key skills and qualifications needed to thrive as an Apache Airflow Python developer?

To thrive as an Apache Airflow Python Developer, you need expertise in Python programming, workflow orchestration concepts, and a solid understanding of data engineering principles, often backed by a degree in computer science or a related field. Familiarity with Airflow's DAGs, scheduling, and integration with cloud platforms (like AWS or GCP), as well as version control systems such as Git, is typically required. Strong problem-solving, collaboration, and communication skills help you efficiently design workflows and work with cross-functional teams. These competencies are crucial for building reliable, scalable data pipelines that support organizational data needs.

What are some common challenges faced by Apache Airflow Python developers when managing complex data pipelines?

Apache Airflow Python developers often encounter challenges such as handling task dependencies, ensuring reliability in scheduling, and troubleshooting failures in distributed environments. Managing dynamic workflows can require advanced Python scripting and a deep understanding of Airflow's DAG structure. Additionally, developers frequently collaborate with data engineers and analysts to optimize pipeline performance and maintain data integrity, making strong communication and problem-solving skills essential.

What is the difference between Apache Airflow Python vs Data Engineer?

AspectApache Airflow PythonData Engineer
Primary FocusWorkflow orchestration and automationData pipeline development and management
Required SkillsPython scripting, DAG creation, schedulingSQL, ETL processes, cloud platforms
Work EnvironmentData workflows, automation toolsData warehouses, cloud environments
CertificationsPython, Apache Airflow certificationsData engineering certifications (e.g., Google Cloud, AWS)

While Apache Airflow Python specializes in automating and scheduling data workflows using Python, Data Engineers focus on designing, building, and maintaining data pipelines across various platforms. Both roles require Python skills, but Data Engineers typically have broader expertise in databases, cloud services, and data architecture.

Does Apache Airflow use Python?

Yes, Apache Airflow is primarily written in Python and uses Python scripts to define, schedule, and monitor workflows. Python knowledge is essential for developing and maintaining Airflow DAGs and tasks.

What other helpful pages are available for Apache Airflow Python?

Other pages related to Apache Airflow Python:

Infographic showing various Apache Airflow Python job openings in the United States as of September 2026, with employment types broken down into 3% Internship, 88% Full Time, 4% Part Time, and 5% Contract. Highlights an 76% Physical, 5% Hybrid, and 19% Remote job distribution, with an average salary of $141,976 per year, or $68.3 per hour.

Software Engineer Python

Mountain View, CA • On-site

Precision Technologies Corp
See more jobs at Precision Technologies Corpexternal

Contractor

Re-posted 26 days ago

Job description

Role : Software Engineer

Location : Mountain View CA (5 days working)

Minimum Basic Requirements

  • Python Proficiency: Write, update, and maintain Python frameworks and libraries to support data processing and integration tasks.
  • Composer / Apache Airflow: Hands-on experience with Apache Airflow, including updating DAGs, managing multi-node rollouts, and troubleshooting issues
  • Code Management: Use Git and GitHub for source control, code reviews, and version management.
  • GCP Proficiency: Extensive experience working with GCP services (e.g., Big Query, Cloud Dataflow, Pub/Sub, Cloud Storage, Monitoring). Knowledge of resource labeling and automation through SDKs or APIs.
  • Software Engineering: Strong understanding of software engineering best practices, including version control (Git), collaborative development (GitHub), code reviews, and CI/CD.
  • Problem-Solving: Excellent problem-solving skills with the ability to tackle complex data engineering challenges.
  • Communication: Excellent stakeholder communication skills, with the ability to interface directly with data scientists, platform engineers, and other clients to explain complex technical details, coordinate rollouts, triage issues, and provide updates
  • Bachelor’s or master’s degree in computer science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field or software development training program

What you’ll do

  • Develop and enhance Python frameworks and libraries to support cost tracking, data processing, data quality, lineage, governance, and MLOps.
  • Implement data processing optimizations to reduce the cost of our larger training data and features pipelines.
  • Build scalable features and training data batch pipelines leveraging Big query, Dataflow and Composer scheduler/executor framework on Google Cloud Platform.
  • Implement monitoring, logging, and alerting systems to ensure the reliability and stability of our data and ml infrastructure and pipelines.
  • Plan and oversee infrastructure rollouts, including phased deployments, validation, and rollback strategies.
  • Act as primary point of contact for Data Scientists, ML Engineers, and other stakeholders handling rollout coordination, communications, and issue resolution.
  • Collaborate with ML Platform engineers to ensure seamless integration of updates into workflows.
  • Document processes and changes, providing clear runbooks and handoffs for ongoing support.

Preferred Requirements

  • Python Mastery: Strong Python development background, with demonstrated experience maintaining and extending production-grade SDKs or internal libraries
  • Change Management: Experience with infrastructure rollout and change management, including phased deployments, validation, and rollback strategies
  • Resiliency: Comfortable working in fast-moving ML/AI platform environments, where reliability, transparency, and client experience are key
  • Batch Pipelines: Experience with building, deploying, and maintaining production batch pipelines processing and publishing petabytes of data.