Senior Platform Engineer - Azure Data & AI Platform

Overview

In this role you build and run Howden’s Azure-based data and AI platform. You work hands-on across infrastructure, data, and ML ops to enable secure, scalable platforms for engineers and data/AI teams. You’ll implement landing zones, Databricks governance, and MLOps pipelines while embedding devsecops practices. This is a pragmatic, code-driven role with a strong impact on cloud cost, security, and platform usability. You’ll collaborate across functions to ship reliable, observable platforms that empower analytics and AI initiatives.

Pay / Benefits

• remote-first with occasional in-office travel
• learning budget for conferences and certifications
• equipment and licenses provided
• blameless post-mortems
• RFCs for significant changes
• outcomes-oriented culture

Responsibilities

• Design and implement Azure landing zones, management groups and subscription architecture
• Build hub-spoke network topologies with security controls
• Implement governance with Azure Policy, RBAC and IAM frameworks
• Manage identity using Entra ID and service principals
• Set up monitoring and cost controls with Azure Monitor, Log Analytics, Application Insights
• Deploy and configure Azure Databricks workspaces with Unity Catalog; support pipeline development
• Establish Databricks best practices: policies, scheduling, notebooks organization
• CI/CD for Databricks notebooks, jobs, infrastructure
• Work with data engineers on performance, cost, and platform capabilities
• Create platform tooling, dashboards and self-service automation
• Participate in on-call rotation for platform incidents
• Develop ML model deployment pipelines using Azure ML/MLflow; inference endpoints
• Integrate AI services (Azure OpenAI, Cognitive Services) and enforce guardrails

Key requirements

• 5+ years in platform/infrastructure engineering
• Deep Azure knowledge (networking, IAM, storage, compute, PaaS)
• Databricks experience with Unity Catalog and cost management
• Terraform (or Bicep) IaC; modular, scalable project structure
• CI/CD pipelines in Azure DevOps or GitHub Actions
• Containerization practices for apps and ML models
• Security-first mindset with defense in depth and least privilege
• MLOps fundamentals: training vs inference, experiment tracking, versioning
• Python and/or PowerShell for automation
• Observability beyond basics: distributed tracing and log aggregation

• Pragmatism and tool selection based on the problem
• Documentation discipline and clear communication
• Autonomy and ownership: build it, run it, support it
• Azure landing zones / CAF experience
• Microsoft Purview for data governance (preferred)
• Delta Lake and Spark optimization (preferred)

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