Lead Solution Architect, Customer Analytics, EDW & AI

Establish architecture standards and reference implementations across Snowflake, Databricks, data modeling, orchestration / ELT, APIs, front-end consumption, and customer-facing AI capabilities.

Translate business requirements into scalable architecture designs that align with enterprise architecture principles, business objectives, and technology standards.

• Evaluate technology options, platforms, and architectural patterns to recommend secure, scalable, and compliant solution components.

Evaluate technology options, platforms, and architectural patterns to recommend secure, scalable, and compliant solution components.

Lead design reviews and provide architectural direction for high-impact initiatives across data, application, AI, and cloud platforms.

• Ensure solutions meet non-functional requirements for availability, performance, security, observability, compliance, operability, and cost efficiency.

Customer Analytics, EDW & Data Platform Architecture

Lead EDW integration architecture by defining resilient ELT / ETL patterns, data contracts, lineage, quality checks, governance controls, and measurable service expectations.

Model data for analytics using facts, dimensions, semantic layers, and data products that support BI tools, APIs, reporting applications, and AI consumption patterns.

• Drive performance tuning, partitioning, clustering, caching, and cost governance across storage, compute, and query layers.

Drive performance tuning, partitioning, clustering, caching, and cost governance across storage, compute, and query layers.

Design architecture patterns that allow structured and unstructured enterprise data to be securely consumed by AI solutions through governed RAG pipelines.

Define metadata, lineage, governance, and knowledge-management strategies to improve trust, retrieval quality, and response grounding.

AI, RAG & Agentic AI Architecture

Define architecture patterns for AI-powered analytics products, including conversational analytics, natural language query experiences, automated insight generation, intelligent reporting, and autonomous workflow orchestration.

Design scalable Agentic AI architectures that leverage LLMs, multi-agent orchestration frameworks, tool calling, memory management, enterprise APIs, and secure execution patterns.

Establish reference architectures for RAG solutions, including document ingestion, chunking strategy, embedding generation, vector search, semantic retrieval, prompt orchestration, grounding, and evaluation frameworks.

Lead integration of enterprise data products with Azure OpenAI, Azure AI Foundry, Azure AI Search, vector databases, and external AI APIs.

Define and promote AI governance practices covering responsible AI, model monitoring, prompt safety, privacy, auditability, explainability, and risk management.

Engineering Leadership & Delivery Enablement

Partner with full-stack engineering teams to shape service boundaries, API contracts, integration patterns, and secure data consumption models.

Guide engineering teams in building AI services, copilots, intelligent agents, and conversational experiences integrated with customer-facing analytics products.

Create architecture decision records, solution diagrams, API specifications, data contracts, standards, and knowledge-sharing artifacts.

Mentor engineers, data engineers, and architects on architecture patterns, secure coding, testing, reliability, logging, metrics, tracing, alerting, incident response, and operational readiness.

Drive practical execution from architecture documents to working reference implementations and reusable production-grade patterns.

Security, Compliance & Governance

Embed security by design, including authentication, authorization, least privilege, encryption, secrets management, secure APIs, and secure data sharing.

Define controls for secure enterprise data use in GenAI applications, including vector stores, embeddings, prompts, LLM interactions, model outputs, and auditability.

• Support governance for PII / PHI, regulatory requirements, security controls, and audit readiness.

Support governance for PII / PHI, regulatory requirements, security controls, and audit readiness.

• Lead architecture reviews, risk assessments, threat modeling, and secure-by-default design reviews for data and AI products.

Lead architecture reviews, risk assessments, threat modeling, and secure-by-default design reviews for data and AI products.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent experience.

Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent experience.

Typically 10+ years of architecture / engineering experience, including sustained leadership of enterprise-scale, cross-platform programs.

Experience designing, governing, and delivering customer-facing analytics, reporting, or data-product platforms.

Hands-on experience with large-scale enterprise data warehouse integrations, data architecture, data modeling, ELT / ETL patterns, data quality, lineage, governance, and privacy.

Experience with Snowflake, Databricks, Spark, SQL, semantic models, data products, and analytics platforms.

Experience with modern service and API design, including REST / JSON, authentication, authorization, versioning, error handling, and secure API consumption.

Experience designing and deploying Generative AI solutions in enterprise environments.

Demonstrated experience with RAG architectures, including vector databases, embeddings, document indexing, semantic search, retrieval orchestration, and prompt workflows.

Practical experience with Agentic AI solutions, including multi-agent systems, orchestration frameworks, tool integration, memory patterns, reasoning workflows, and autonomous task execution.

Experience with Azure OpenAI, Azure AI Foundry, Azure AI Search, LLM APIs, embedding APIs, vector databases, or related AI services.

Strong understanding of prompt engineering, model evaluation, hallucination mitigation, guardrails, Responsible AI controls, and AI application observability.

• Ability to translate complex architecture decisions into clear recommendations for technical and non-technical stakeholders.

Ability to translate complex architecture decisions into clear recommendations for technical and non-technical stakeholders.

Core Competencies

Enterprise Architecture Leadership: Sets architecture direction, defines standards, and guides teams toward secure, scalable, and business-aligned solutions.

Systems Thinking: Balances customer experience, data quality, security, scalability, performance, cost, compliance, and operability.

AI Architecture & Governance: Designs responsible AI ecosystems that integrate enterprise data, analytics platforms, APIs, and intelligent agents.

Technical Influence: Builds consensus across product, engineering, data, security, operations, and executive stakeholders.

Pragmatic Execution: Moves from architecture strategy to reference implementations, reusable patterns, and production-ready delivery.

Preferred Qualifications

• Experience integrating analytics with BI tools such as Power BI, Google Looker, semantic layers, data catalogs, and governance tooling.

Experience integrating analytics with BI tools such as Power BI, Google Looker, semantic layers, data catalogs, and governance tooling.

Experience with cloud data platforms and services, including Snowflake on Azure, Databricks, Azure Data Factory, object storage, and event streaming platforms such as Kafka.

Experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, Microsoft Fabric AI capabilities, Semantic Kernel, LangChain, LangGraph, AutoGen, CrewAI, or similar frameworks.

Experience implementing vector databases and semantic retrieval platforms such as Azure AI Search, Pinecone, Weaviate, Chroma, or equivalent technologies.

Experience building conversational analytics, AI copilots, knowledge assistants, and intelligent workflow automation solutions.

Experience with AI evaluation frameworks, retrieval quality metrics, grounding validation, prompt testing, safety evaluation, and production model monitoring.

Experience deploying AI applications using containerized and cloud-native architectures on Azure.

• Experience in healthcare IT, regulated industries, or other large-scale enterprise environments.

Relocation assistance / allowance is not budgeted for this position

• 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 click here.

Our Base Pay Range for this position

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