Lead Solution Architect, Customer Analytics, EDW & AI
$122,100–$162,800 year
On-siteMississauga, Ontario, Canada
Job Summary
Define enterprise-grade architecture for customer analytics, enterprise data warehouse integrations, and AI-enabled data products. Establish standards and reference implementations across Snowflake, Databricks, and Azure platforms to guide full-stack engineering teams in designing scalable, secure, and reliable platforms. Lead design reviews, migration plans, and fit-gap analysis while ensuring solutions meet rigorous security, performance, and compliance expectations. Architect RAG pipelines, Agentic AI frameworks, and semantic layers that transform structured and unstructured data into actionable insights for customers. This role operates with high autonomy to harmonize initiatives with McKesson's strategic objectives and advance measurable outcomes aligned with the company's health and well-being mission.
Required 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
Desired 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
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