Lead Data Architect

Our Decision Intelligence (DI) team is seeking a Senior / Lead Data Architect to drive enterprise data strategy and accelerate AI‑enabled transformation across McKesson. DI plays a critical role in enabling data‑driven change and delivering measurable business value through high‑quality data, advanced analytics, and intelligent automation.

This role will define and evolve the enterprise‑wide data and semantic architecture required to support AI‑driven insights, agentic automation, and next‑generation data products. The ideal candidate is a strategic thought partner, a hands‑on architect, and a leader capable of translating business outcomes into scalable technical solutions.

• Architect canonical data domains across customer, product, pricing, supply chain, contracting, and financial performance.
• Design semantic layers, business ontologies, subject‑area models, and metric definition frameworks to power enterprise AI agents and decisioning systems.
• Define architectural principles for data interoperability, lineage, access control, security, and multi‑cloud integration.
• Align data platform and architecture decisions with the USPD AI Roadmap and enterprise AI strategy.

Establish standards and patterns for:

• Vector search
• Metadata‑driven orchestration

Provide architectural oversight and strategic guidance across enterprise data products including:

• Finance, Pricing, and Supply Chain Data Products
• FIA
• ContractIQ
• Specialty Leakage Agents
• Design a robust, scalable, and interoperable data environment that supports AI‑ready, governed, high‑quality enterprise data.
• Influence programs and project teams on best practices related to data quality, architecture, modeling, observability, and governance.
• Leverage data architecture frameworks to translate complex relational entities into business cases, use cases, and AI‑enablement requirements.
• Partner with product, engineering, and analytics leaders to accelerate data product creation and improve enterprise decision intelligence maturity.

• Architect complex distributed data systems that ensure scalability, performance, reliability, and real‑time integration across business‑critical operations.
• Design and govern enterprise‑wide data models, data flows, reference architectures, and integration patterns.
• Entity relationship diagrams (ERDs)
• Data flow diagrams
• Comprehensive data dictionaries and metadata documentation
• Ensure optimal functioning of AI/ML pipelines, including data quality controls, observability patterns, and architecture for low‑latency analytics.
• Guide engineering teams on reusable patterns for ingestion, transformation, curation, semantic enrichment, and operationalization.

• 7+ years of experience in data engineering, data architecture, or enterprise data platform development.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field.

• Demonstrated experience with Enterprise data modeling, semantic layers, canonical domains
• Databricks, Snowflake, MDM platforms, SAP, Salesforce/Conga
• Designing intuitive architectural patterns to simplify complex data landscapes.
• Strong understanding of data quality frameworks, governance, lineage, metadata, and regulatory compliance.

• Ownership‑driven leader with a track record of guiding engineering teams through delivery.
• Acts as a change champion, elevating architecture maturity and influencing cross‑functional adoption of best practices.

• Strong analytical capability and the ability to develop long‑term data strategies aligned to enterprise objectives and future‑state AI readiness.

• Creative, innovative problem solver capable of architecting solutions for highly complex data and AI challenges.