Lead Data Architect – Data & AI Platform

Position Summary

The Lead Data Architect – Data & AI Platform provides enterprise technical leadership to define, govern, and evolve McKesson’s Intelligent Data Platform, with a focus on Azure Databricks, AI-ready data, and Retrieval-Augmented Generation (RAG) capabilities.

This role operates as a senior individual contributor and recognized subject matter expert, responsible for establishing architecture standards, platform patterns, and data modeling strategies that enable secure, scalable, and compliant data and AI solutions across the enterprise.

The Lead Data Architect drives cross-domain alignment through canonical data models and shared data structures, ensuring consistency and interoperability across business units and platforms. This role partners closely with engineering, analytics, and business teams to translate complex capabilities into reusable, AI-ready data assets.

As a key technical leader, this role influences enterprise data strategy, guides solution design, mentors architects and engineers, and ensures architectural decisions deliver measurable business impact in a regulated healthcare environment.

Key Responsibilities

Platform Architecture & Strategy

• Define and maintain the Azure Databricks reference architecture supporting AI/ML data preparation, RAG grounding, orchestration, telemetry, and governance
• Establish and enforce platform standards and guardrails, including workspace patterns, Unity Catalog design, compute policies, and cost optimization strategies
• Lead evaluation and adoption of emerging data and AI architecture patterns aligned to enterprise strategy

Data Modeling & Information Architecture

• Define and govern conceptual, logical, and physical data models for enterprise-scale platforms
• Establish canonical data structures, business glossaries, and cross-domain standards ensuring interoperability and reuse
• Standardize modeling approaches (e.g., normalized, dimensional, Data Vault, domain-driven design) across domains
• Ensure data models support AI/ML, analytics, and operational use cases with consistency, traceability, and compliance

AI & Data Platform Enablement

• Standardize embedding, feature, vector, and contextual data design to enable scalable RAG and AI use cases
• Design secure and governed integration patterns between Databricks and downstream AI services and applications
• Partner with business and domain teams to translate capabilities into AI-ready, production-scale data assets

Governance, Security & Compliance

• Ensure Unity Catalog serves as the system of record for data access, enforcing fine-grained permissions, masking, lineage, and auditability
• Apply secure-by-design and Zero Trust principles to all data and AI architectures
• Govern data lifecycle management, including metadata standards, lineage, schema evolution, and versioning
• Ensure compliance with regulatory, privacy, and data stewardship requirements

Operational Excellence & Quality Engineering

• Embed quality engineering and validation into AI pipelines using MLflow, evaluation datasets, telemetry, and drift monitoring
• Define standards for production readiness including workflows, monitoring, SLAs, and KTLO transitions
• Drive continuous improvement in platform reliability, scalability, and observability

Technical Leadership & Influence (P5 Expectations)

• Act as a lead technical authority across data architecture initiatives
• Conduct architecture reviews and provide guidance on standards, patterns, and best practices
• Mentor and coach architects, engineers, and data professionals
• Influence cross-functional teams and senior stakeholders to align architecture to business outcomes

Minimum Job Qualifications (Knowledge, Skills, & Abilities)

• Expert knowledge of enterprise data architecture, data modeling, and cloud-native platforms
• Deep expertise in data governance, metadata management, lineage, and observability
• Strong understanding of AI/ML data patterns including RAG and vector retrieval architectures
• Advanced proficiency in conceptual, logical, and physical data modeling techniques
• Experience designing secure, scalable Azure-based data platforms (Databricks preferred)
• Ability to define and communicate architecture standards, roadmaps, and strategies
• Strong skills in stakeholder engagement, influencing, and cross-functional collaboration
• Excellent written and verbal communication skills for technical and executive audiences

Required Qualifications

• 10+ years of experience in data, platform, or enterprise architecture within large-scale environments
• Proven experience designing and governing enterprise data platforms and architectures
• Hands-on experience with Azure Databricks, data lakehouse architectures, and scalable data systems
• Strong experience in data modeling and cross-domain data integration
• Experience working in regulated environments with compliance, privacy, and governance requirements

Preferred Qualifications

• Experience defining enterprise information models, business glossaries, and semantic layers
• Familiarity with data modeling methodologies such as Data Vault, dimensional modeling, or domain-driven design
• Hands-on experience with Unity Catalog, MLflow, Vector Search, and Databricks ecosystem
• Experience integrating data platforms with metadata, lineage, and governance tools
• Azure certifications such as AZ-305, AI-102, or AZ-500

Business Experience

• Bachelor’s degree or equivalent in Computer Science, Information Systems, Data Science, Engineering, or related field
• Typically requires 10+ years of relevant professional experience in data architecture or related disciplines
• Demonstrated ability to lead enterprise-wide architecture initiatives and influence strategic direction
• Experience operating within regulated industries (e.g., healthcare, pharma, life sciences) preferred

Work Model / Physical Requirements

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 markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In 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, please

Our Base Pay Range for this position

$122,100 - $162,800

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