Airflow Platform Engineer

Jade Global

Airflow Platform Engineer at Jade Global designing, developing, and optimizing Airflow DAGs. Supporting Kubernetes and improving platform stability for enterprise-level data workflows.

Posted 7/1/2026full-timeHyderabad • 🇮🇳 IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing Apache Airflow DAGs for enterprise data workflows, with strong proficiency in Python and Kubernetes. Capable of implementing CI/CD practices and troubleshooting complex orchestration issues to enhance platform stability and performance.

Highest-signal resume keywords

Apache AirflowPython ProgrammingKubernetes (AKS)CI/CD PipelinesDBT Pipelines

ATS Keywords

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Hard Skills

DAG DevelopmentOperators (PythonOperator, SQL Operators, API/HTTP Operators, KubernetesPodOperator)Error HandlingUnit TestingIntegration Testing

Tools & Technologies

Azure ServicesGitJenkinsDocker

Industry Keywords

Data WorkflowsOrchestration PatternsProduction EnvironmentsMonitoringAlerting

Tech Stack

Tools & technologies

AirflowApacheAzureCloudDockerJenkinsKubernetesPythonSQLVault

About the role

Key responsibilities & impact

Design, develop, and optimize Airflow DAGs for enterprise use cases
Demonstrate strong experience with operators such as PythonOperator, SQL operators, API/HTTP operators, KubernetesPodOperator etc.
Apply advanced DAG patterns including dynamic DAGs, branching, sensors, retries, and error handling
Troubleshoot DAG failures, performance issues, and integration challenges
Enable orchestration of modern data workflows including DBT pipelines
Provide reusable templates, best practices, and onboarding support to drive self-service adoption
Support Airflow platform deployed on AKS, covering scheduler, webserver/API, workers, and metadata services
Troubleshoot issues related to task execution, scheduling delays, and resource bottlenecks
Diagnose Kubernetes-level issues involving pods, networking, storage, and RBAC
Work with Azure services such as Key Vault, Storage, and networking
Support CI/CD pipelines for DAG and container-based deployments
Improve platform stability, monitoring, and alerting

Requirements

What you’ll need

4 - 6 years of experience
Strong experience with Apache Airflow (3.x or later) in production environments
Proven experience in DAG development using operators and orchestration patterns
Working knowledge of Kubernetes (AKS preferred) to support Airflow runtime
Expert-level Python knowledge for building, debugging, and maintaining production-grade workflows and supporting libraries
Expertise in writing unit, integration, and DAG validation tests for Airflow workflows, including mocking operators, validating dependencies, and ensuring reliability in CI/CD pipelines
Experience with DBT and modern data pipelines
Experience with CI/CD tools (Git, Jenkins, Docker)
Familiarity with Azure or other cloud platforms

Benefits

Comp & perks

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