Databricks Platform Manager - ML Ops

BSI

Milton Keynes, UK

7 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.collegerecruiter.com

Tech stack

Artificial Intelligence Business Analytics Applications Automation of Tests Continuous Integration Information Engineering Identity and Access Management Machine Learning Release Management Management of Software Versions Build Management Data Lakes Machine Learning Operations

+1 more

Databricks

Job description

Own and manage the day-to-day operation of the Databricks Lakehouse Platform and MLOps services, ensuring high availability, performance, security, and user satisfaction.
Lead platform service management activities, including incident, problem, change, and request processes, while coordinating support and escalation procedures.
Develop and maintain standardized MLOps frameworks, tooling, and best practices to support the end-to-end machine learning lifecycle.
Collaborate with Data Engineering and Data Science teams to operationalize ML models through CI/CD, automated testing, deployment, and monitoring capabilities.
Ensure robust security, compliance, and governance controls across the platform, including access management, data protection, audit readiness, and risk mitigation.
Own the Databricks platform roadmap, driving continuous improvement, technical standards, cost optimization, and scalable architecture patterns.
Manage vendor and supplier relationships, overseeing service performance, support activities, release planning, and commercial considerations.
Act as the primary platform stakeholder interface, providing guidance, training, documentation, and enablement to help teams effectively build and deploy data and AI solutions.

Requirements

Proven experience managing data/analytics platforms and/or MLOps capabilities in a complex enterprise environment.
Hands-on experience operating Databricks (workspace administration, cluster policies, jobs, Delta Lake, Unity Catalog, access controls, and production support).
Experience implementing MLOps practices across the model lifecycle (CI/CD, versioning, monitoring, reproducibility).
Strong stakeholder management; able to translate business outcomes into platform priorities and service commitments.
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.