Lead Solution Architect, Customer Analytics, EDW & AI

Job description

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 tomorrow’s health today, we want to hear from you. Job Title: Lead Solution Architect – Customer Analytics, Enterprise Data Warehouse & AI Position Summary The Lead Solution Architect – Customer Analytics, Enterprise Data Warehouse & AI is responsible for establishing enterprise-grade technical architecture that delivers sustained business value across system-wide, mission-critical programs. This role owns architectural components and ensures alignment to future-state technology vision, directs fit-gap analysis, validates migration plans, and evaluates technology platforms and architectural patterns to ensure solutions meet rigorous security, performance, reliability, compliance, and operability expectations. This role will focus on customer-facing analytics, enterprise data warehouse integrations, reporting products, APIs, dashboards, semantic models, and AI-enabled data products. The architect will guide full-stack engineering and EDW teams to design scalable, secure, and reliable platforms that deliver actionable insights to customers through dashboards, APIs, semantic layers, and intelligent AI-powered experiences. The successful candidate will bring deep experience in data analytics, large-scale EDW integrations, cloud-native architecture, software delivery, and enterprise AI solutions, including Retrieval-Augmented Generation, Agentic AI frameworks, LLM orchestration, vector search, AI APIs, and Azure-based AI governance. Operating with a high degree of autonomy, the Lead Solution Architect will consult across multiple domains, harmonize initiatives with enterprise architecture, set and enforce standards, provide clear technical recommendations to non-technical stakeholders, and advance measurable outcomes aligned with McKesson’s strategic objectives. Key Responsibilities Enterprise Solution Architecture Define and own the target architecture for customer analytics, enterprise data warehouse integrations, reporting products, APIs, semantic models, dashboards, and AI-powered insights. 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. 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. 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. Architect semantic layers, data products, and knowledge graphs that improve contextual retrieval and reasoning across customer analytics platforms. 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. Establish best practices for prompt engineering, model evaluation, AI observability, retrieval quality measurement, agent testing, and continuous model improvement. 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. Partner across product, data governance, security, customer success, engineering, and business stakeholders to translate business outcomes into technical roadmaps. 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. Lead architecture reviews, risk assessments, threat modeling, and secure-by-default design reviews for data and AI products. Ensure solution architecture decisions align with enterprise standards, architecture guidelines, and governance principles. Minimum Qualifications 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 depl