Job Description
Key Responsibilities:
Analyze and optimize the current Apache Airflow environment, identifying performance bottlenecks and implementing best practices for orchestration and scheduling.
Design and implement scalable, modular, and reusable DAGs (Directed Acyclic Graphs) to support complex data workflows.
Collaborate with data engineers and platform teams to integrate Airflow with Azure Data Factory, Azure Databricks, and other Azure-native services.
Develop and maintain CI/CD pipelines using Azure DevOps for Airflow DAG deployment, testing, and version control.
Establish monitoring, alerting, and logging standards for Airflow jobs to ensure operational excellence and rapid incident response.
Provide architectural guidance and hands-on support for new data pipeline development using Airflow and Azure services.
Document Airflow configurations, deployment processes, and operational runbooks for internal teams.
Mentor engineers and contribute to knowledge-sharing sessions on orchestration and workflow management.
Required Skills and Qualifications:
Proven experience as an Apache Airflow SME or Lead Developer in a production-grade environment.
Strong understanding of Airflow internals, including scheduler, executor types (Celery, Kubernetes), and plugin development.
Experience with workload orchestration and autoscaling using KEDA (Kubernetes-based Event Driven Autoscaler), and familiarity with Celery for distributed task execution and background job processing, particularly in data pipeline or microservices environments
Hands-on experience with Azure cloud services, especially Azure Data Factory, Azure Databricks, Azure Storage, and Azure Synapse.
Proficiency in designing and deploying CI/CD pipelines using Azure DevOps (YAML pipelines, release management, artifact handling).
Solid programming skills in Python, with experience in writing modular, testable, and reusable code.
Familiarity with containerization (Docker) and orchestration (Kubernetes) as it relates to Airflow deployment.
Experience with monitoring tools (e.g., Prometheus, Grafana, Azure Monitor) and log aggregation (e.g., ELK, Azure Log Analytics).
Strong problem-solving skills and the ability to work independently in a fast-paced, agile environment.
Excellent communication skills and the ability to collaborate effectively with cross-functional teams.
Analyze and optimize the current Apache Airflow environment, identifying performance bottlenecks and implementing best practices for orchestration and scheduling.
Design and implement scalable, modular, and reusable DAGs (Directed Acyclic Graphs) to support complex data workflows.
Collaborate with data engineers and platform teams to integrate Airflow with Azure Data Factory, Azure Databricks, and other Azure-native services.
Develop and maintain CI/CD pipelines using Azure DevOps for Airflow DAG deployment, testing, and version control.
Establish monitoring, alerting, and logging standards for Airflow jobs to ensure operational excellence and rapid incident response.
Provide architectural guidance and hands-on support for new data pipeline development using Airflow and Azure services.
Document Airflow configurations, deployment processes, and operational runbooks for internal teams.
Mentor engineers and contribute to knowledge-sharing sessions on orchestration and workflow management.
Required Skills and Qualifications:
Proven experience as an Apache Airflow SME or Lead Developer in a production-grade environment.
Strong understanding of Airflow internals, including scheduler, executor types (Celery, Kubernetes), and plugin development.
Experience with workload orchestration and autoscaling using KEDA (Kubernetes-based Event Driven Autoscaler), and familiarity with Celery for distributed task execution and background job processing, particularly in data pipeline or microservices environments
Hands-on experience with Azure cloud services, especially Azure Data Factory, Azure Databricks, Azure Storage, and Azure Synapse.
Proficiency in designing and deploying CI/CD pipelines using Azure DevOps (YAML pipelines, release management, artifact handling).
Solid programming skills in Python, with experience in writing modular, testable, and reusable code.
Familiarity with containerization (Docker) and orchestration (Kubernetes) as it relates to Airflow deployment.
Experience with monitoring tools (e.g., Prometheus, Grafana, Azure Monitor) and log aggregation (e.g., ELK, Azure Log Analytics).
Strong problem-solving skills and the ability to work independently in a fast-paced, agile environment.
Excellent communication skills and the ability to collaborate effectively with cross-functional teams.
💡 Quick Summary
Seeking a career-building opportunity? The Airflow SME position is now open for candidates interested in the IT Engineer & Developer Jobs sector. This role in London offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in IT Engineer & Developer Jobs is a plus.