Senior Platform Engineer - Azure Data & AI Platform

Who are we?

Howden is a global insurance group with employee ownership at its heart. Together, we have pushed the boundaries of insurance. We are united by a shared passion and no-limits mindset, and our strength lies in our ability to collaborate as a powerful international team comprised of 24,000 employees spanning over 56 countries.

People join Howden for many different reasons, but they stay for the same one: our culture. It’s what sets us apart, and the reason our employees have been turning down headhunters for years. Whatever your priorities – work / life balance, career progression, sustainability, volunteering – you’ll find like-minded people driving change at Howden.

Senior Platform Engineer - Azure Data & AI PlatformThe Role

We are looking for senior platform engineers to build and operate our Azure-based data and AI platform. This is hands-on infrastructure and platform work - you'll be writing Terraform, designing network architecture, implementing MLOps (AI model) pipelines, and establishing DevSecOps patterns that engineering teams can use.

This isn't a "thought leadership" or "strategy" role. You'll be in the code, in the CLI, and in the infrastructure daily.

What You'll Actually DoAzure Platform Engineering (40%)

• Design and implement Azure landing zones, management groups, and subscription architecture

• Build and maintain hub-spoke network topologies with proper segmentation and security controls

• Implement Azure Policy, RBAC, and governance frameworks that balance security with developer productivity

• Manage identity and access using Entra ID, service principals, managed identities

• Establish monitoring, logging, and alerting with Azure Monitor, Log Analytics, and Application Insights

• Cost management and FinOps practices - keeping cloud spend under control without hamstringing teams

Databricks & Data Platform (30%)

• Deploy and configure Azure Databricks workspaces with Unity Catalog for data governance

• Assisting both data and AI teams with pipeline development

• Establish Databricks best practices: cluster policies, job scheduling, notebook standards, workspace organization

• Integrate Databricks with ADLS Gen2, Azure SQL, Synapse, and other data services

• Set up and maintain CI/CD for Databricks notebooks, jobs, and infrastructure

• Work with data engineers on performance optimization, cost control, and platform capabilities

MLOps & AI Platform (20%)

• Build ML model deployment pipelines using Azure ML, Databricks MLflow, or both