Lead Solution Architect, Customer Analytics, EDW & AI

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.

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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 McKessons 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

• Bachelors 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.

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.

• Communication & Storytelling: Simplifies complex trade-offs and clearly communicates risks options and recommendations to technical and non-technical audiences.

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

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 McKessons 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 pleaseclick here.

Our Base Pay Range for this position

$122100 - $162800

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McKesson is an Equal Opportunity Employer

McKesson provides equal employment opportunities to applicants and employees without regard to race color religion sex sexual orientation gender identity national origin protected veteran status disability age genetic information or any other legally protected category. For additional information on McKessons full Equal Employment Opportunity policies visit our Equal Employment Opportunity page.

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Required Experience:

Staff IC

Employment Type : Full-Time
Experience: years
Vacancy: 1
Monthly Salary Salary: 122100 - 162800