Optimization Specialist

Optimization Specialist

Posted 7 days ago by iXceed Solutions

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Negotiable
Undetermined
Undetermined
London Area, United Kingdom
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Agile Methodology Airflow Apache Airflow Autoscaling Azure Cloud Services Azure Databricks Azure Data Factory Azure DevOps Azure Kubernetes Service Azure Monitor Cloud Computing Cloud Services Cloud Technology Containerisation Continuous Integration and Continuous Delivery Cyber Incident Response Databricks Data Engineering Data Pipeline DevOps Docker (Software) Elastic (ELK) Stack Generic Programming Grafana Kubernetes Management Microservices Microsoft Azure Operational Excellence Prometheus (Software) Python (Programming Language) Release Management Scalability Scheduling Software Development Version Control Workflows YAML

Summary: The role of Airflow Optimization Specialist focuses on enhancing the existing Apache Airflow environment within a data engineering team, emphasizing reliability, performance, and scalability. The candidate will leverage expertise in Azure cloud services and Azure DevOps to develop orchestration frameworks for enterprise-scale data pipelines. This position requires collaboration with various teams to integrate Airflow with Azure-native services and establish best practices for deployment and monitoring. The ideal candidate will also mentor engineers and document processes for internal teams.

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, logging, 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.

Key Skills:

  • 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.

Salary (Rate): undetermined

City: London Area

Country: United Kingdom

Working Arrangements: undetermined

IR35 Status: undetermined

Seniority Level: undetermined

Industry: IT

Detailed Description From Employer:

Job description Airflow Optimization Specialist – Azure Data Platform Objective: We are seeking an experienced and highly skilled Apache Airflow Subject Matter Expert (SME) to join our data engineering team. The primary objective of this role is to fine-tune and optimize our existing Airflow environment, ensuring high reliability, performance, and scalability. The ideal candidate will also bring strong expertise in Azure cloud services and Azure DevOps to design, solution, and develop robust orchestration frameworks that support our enterprise-scale data pipelines.

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, ing, 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.
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