ReactJS with Azure developer

Job Description
ReactJS with Azure (Majority work), Python, Airflow, Spark with Kubernetes. And ETL Data knowledge.

The ideal candidate is a ReactJS frontend engineer with Azure cloud deployment expertise who can also design and orchestrate data workflows in Airflow, perform Python scipting and run Spark jobs on Kubernetes. They should be comfortable with Helm, Docker, and CI/CD pipelines for both application and data workloads. In addition to frontend work, the candidate will collaborate with Data Engineering team to deploy and orchestrate workflows in Apache Airflow and run Spark jobs on Kubernetes for large-scale data processing.

• This is a cross-functional role 70% frontend & Azure deployment, 30% data workflow orchestration and Kubernetes-based big data processing.
• Candidate Profile ReactJS + Azure (Primary), Airflow, Spark on Kubernetes
• 1. Core Frontend Expertise (Primary)
• ReactJS (3+ years) building scalable, component-based SPAs.
• Strong in JavaScript (ES6+) and TypeScript.
• Experience with state management (Redux, Zustand, or Recoil).
• UI frameworks: Material-UI, Ant Design, or TailwindCSS.
• REST API and GraphQL integration.
• Performance optimization (lazy loading, memoization, code splitting).
• Unit testing with Jest, React Testing Library, or Cypress.
• 2. Azure Cloud Skills (Majority Work)
• Azure App Services and Azure Static Web Apps for frontend hosting.
• Azure Kubernetes Service (AKS) deploying containerized apps.
• Azure Container Registry (ACR) building and pushing Docker images.
• Azure Key Vault secure secrets management.
• Azure DevOps Pipelines CI/CD for React apps ([example pipeline here]5).
• Familiarity with Helm charts for AKS deployments ([example full-stack React + AKS Helm setup]4).
• Azure Storage (Blob, Table) for static assets and logs.
• 3. Airflow (Workflow Orchestration)
• Deploying Apache Airflow on Kubernetes (AKS) using Helm ([guide here]2).
• Writing DAGs in Python for ETL and data processing.
• Integrating Airflow with Azure Blob Storage, Data Lake, or SQL DB.
• Using KubernetesExecutor for dynamic scaling ([example Airflow + PySpark on K8s]3).
• 4. Spark with Kubernetes
• Running PySpark jobs on Kubernetes clusters.
• Experience with SparkSubmitOperator in Airflow for big data pipelines.
• Optimizing Spark jobs for performance and cost.
• Familiarity with persistent volumes and RBAC in Kubernetes for Spark workloads.
• 5. DevOps & Containerization
• Docker multi-stage builds for React and Python/Node apps.
• Helm templating Kubernetes manifests for multi-environment deployments ([multi-stage Helm example]1).
• kubectl managing deployments, services, and pods.
• CI/CD integration with Azure DevOps or GitHub Actions