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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 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.

• Experience aligning product, engineering, security, data, and operations teams to operationalize target architectures and deliver measurable business outcomes.

Preferred Qualifications

• 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.

Physical Requirements: General Office Demands

Relocation assistance / allowance is not budgeted for this position