McKesson

McKesson

Lead Solution Architect, Customer Analytics, EDW & AI

CAN, ON, Mississauga

Sponsorship not specifiedDetected 1 day ago
Full-Stack DevelopmentSQLSnowflakeDatabricksVector DatabasesAzureAPI DevelopmentRESTSparkData AnalysisData EngineeringData VisualizationLLMsRAGAgentic AILangGraphAI OrchestrationCybersecurityIncident ResponseCustomer SuccessHIPAALeadershipCommunication

About the role

  • This role will focus on customer-facing analytics, enterprise data warehouse integrations, reporting products, APIs, dashboards, semantic models, and AI-enabled data products.

Responsibilities

  • Define and own the target architecture for customer analytics, enterprise data warehouse integrations, reporting products, APIs, semantic models, dashboards, and AI-powered insights.
  • Translate business requirements into scalable architecture designs that align with enterprise architecture principles, business objectives, and technology standards.
  • Lead design reviews and provide architectural direction for high-impact initiatives across data, application, AI, and cloud platforms.
  • 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.
  • 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.

Requirements

  • 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.

Nice to have

  • 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 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.
  • Physical Requirements: General Office Demands
  • Relocation assistance / allowance is not budgeted for this position

Compensation

  • The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations.
  • Our Base Pay Range for this position

Benefits

  • McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare.
  • Here, we focus on the health, happiness, and well-being of you and those we serve - we care.
  • 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.

Company info

  • We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards.
  • We are known for delivering insights, products, and services that make quality care more accessible and affordable.
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This listing is sourced directly from McKesson's careers page and normalized into a canonical job model.