Databricks Platform Manager – ML Ops

Overview

As Platform Manager for ML Operations, you own the reliability and security of the MLOps platform that enables data products and ML models at scale. You will work with data engineering, analytics, and security teams to embed standards, automate controls, and improve stability and cost-to-value. This is a delivery-focused role that balances innovation with risk and compliance. You will shape platform governance and drive continuous improvement, delivering measurable impact across the ML lifecycle.

Pay / Benefits

• flexible working
• paid annual leave
• paid sick leave
• health insurance
• pension with company contribution
• learning and development opportunities

Responsibilities

• Oversee day-to-day operation of the Databricks Lakehouse Platform and MLOps services, ensuring high availability, performance, security, and user satisfaction
• Lead platform service management (incident, problem, change, request processes) and coordinate support/escalation
• Develop and maintain standardized MLOps frameworks, tooling, and best practices for end-to-end ML lifecycle
• Collaborate with Data Engineering and Data Science to operationalize ML models via CI/CD, automated testing, deployment, and monitoring
• Ensure robust security, governance, and data protection controls across the platform
• Own the Databricks platform roadmap, driving improvement, standards, cost optimization, and scalable architectures
• Manage vendor and supplier relationships, including service performance and release planning
• Act as primary platform stakeholder interface, providing guidance, training, and enablement for data and AI solutions

Key requirements

• Proven experience managing data/analytics platforms and/or MLOps in a complex enterprise
• Hands-on Databricks experience (workspace admin, cluster policies, jobs, Delta Lake, Unity Catalog, access controls, production support)
• Experience implementing MLOps across model lifecycle (CI/CD, versioning, monitoring, reproducibility)
• Strong stakeholder management; translating business outcomes into platform priorities
• Experience working with security, risk, and governance teams to evidence controls for data and AI services
• Experience managing suppliers and delivery partners including service performance and support processes

• Stakeholder management
• Translating business outcomes into platform priorities
• Collaboration across data engineering, data science, and security teams
• Databricks Lakehouse Platform
• MLOps practices (CI/CD, versioning, monitoring)
• Security, governance, access controls and data protection

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