Lead Data Architect – Data & AI Platform

McKesson•Mississauga, ON

2d•$122,100 - $162,800•Hybrid

About The Position

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.

Requirements

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
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
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

Nice To Haves

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
Experience operating within regulated industries (e.g., healthcare, pharma, life sciences) preferred

Responsibilities

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
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
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
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
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
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