Senior Lead Platform Architect

McKesson is an impact-driven Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights products and services that make quality care more accessible and affordable. Here we focus on the health happiness and well-being of you and those we serve we care.

What you do at McKesson matters. We foster a culture where you can grow make an impact and are empowered to bring new ideas. Together we thrive as we shape the future of health for patients our communities and our people. If you want to be part of tomorrows health today we want to hear from you.

Our Decision Intelligence (DI) team is looking for a Senior / Lead Platform Architect to define and govern the Azure Databricks platform patterns for AI-ready data and RAG across the enterprise including Unity Catalog access controls secure retrieval evaluation/telemetry standards and production readiness guardrails. As part of the DI team you will lead design of AI ready Intelligent Data Platform ensuring consistent governed interoperable data across McKessons platforms and BU segments.

Keys Roles & Responsibilities:

Platform Architecture Leadership:

• Define and maintain the Azure Databricks reference architecture for AI data preparation grounding (RAG) orchestration telemetry and governance.
• Establish Databricks platform standards and guardrails including workspace patterns Unity Catalog design compute policies and cost controls.
• Ensure Unity Catalog is the system of record for AI data access enforcing fine grained permissions data masking lineage and auditability.
• Standardize embedding feature and context management to enable reuse of AI ready data assets across use cases.
• Operate AI intake and onboarding for Databricks workloads ensuring proper classification governance routing and dependency alignment.
• Architect secure integration patterns between Databricks and downstream AI services or applications preventing unapproved data egress.
• Embed quality engineering into AI pipelines using MLflow evaluation datasets telemetry and drift monitoring before production rollout.
• Ensure production readiness and operability of Databricks AI workloads through Jobs/Workflows standards monitoring and KTLO handoff.
• Apply AI security and compliance by design within Databricks including identity enforcement sensitive data protection and audit logging.
• Enable delivery teams through Databricks specific playbooks templates and coaching to accelerate compliant AI adoption.

Minimum Requirement:

Degree or equivalent and typically requires 10 years of relevant experience

Required Qualifications

• 7 years in platform solution or enterprise architecture with hands on experience in Azure Databricks.
• Proven experience designing AI/analytics data platforms including governance security and large scale data access patterns.
• Strong understanding of RAG vector retrieval data governance and observability in production environments.
• Experience working in regulated environments with security privacy and compliance requirements.

Preferred Qualifications

• Experience enabling GenAI or agentic use cases on Databricks.
• Familiarity with Unity Catalog MLflow Vector Search and Azure-native security patterns.
• Azure certifications (AZ 305 AI 102 AZ 500) strongly preferred.

Working Conditions:

• In office requirement we are Flex and Connect with 2 days a week in office

We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors including performance experience and skills equity regular job market evaluations and geographical addition to base pay other compensation such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson pleaseclick here.

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