Lead Solution Architect, Analytics & AI

Salary: $196,000 - 236,000 per year
Requirements:
We hold a bachelors degree in Computer Science, Information Systems, Engineering, or a related discipline, or bring equivalent practical experience. We have typically 10+ years of architecture or engineering experience, with a track record of leading enterprise-scale, cross-platform initiatives. We have experience building and governing customer-facing analytics, reporting, or data product platforms. We bring hands-on expertise in large-scale enterprise data warehouse integrations, data architecture, data modeling, ELT/ETL patterns, data quality, lineage, governance, and privacy. We have experience with Snowflake, Databricks, Spark, SQL, semantic models, data products, and analytics platforms. We understand modern service and API design, including REST/JSON, authentication, authorization, versioning, error handling, and secure API consumption. We have experience designing and deploying Generative AI solutions in enterprise environments. We have practical knowledge of RAG architectures, including vector databases, embeddings, document indexing, semantic search, retrieval orchestration, and prompt workflows. We have experience with Agentic AI solutions, including multi-agent systems, orchestration frameworks, tool integration, memory patterns, reasoning workflows, and autonomous task execution. We have worked with Azure OpenAI, Azure AI Foundry, Azure AI Search, LLM APIs, embedding APIs, vector databases, or similar AI services. We understand prompt engineering, model evaluation, hallucination mitigation, guardrails, Responsible AI controls, and AI application observability. We can turn complex architecture decisions into clear guidance for both technical and non-technical stakeholders. We have experience aligning product, engineering, security, data, and operations teams to deliver target architectures and measurable business outcomes. We demonstrate enterprise architecture leadership and systems thinking, with a focus on secure, scalable, business-aligned solutions.
Responsibilities:
We define and own the target architecture for customer analytics, enterprise data warehouse integrations, reporting products, APIs, semantic models, dashboards, and AI-powered insights. We establish architecture standards and reference implementations across Snowflake, Databricks, data modeling, orchestration/ELT, APIs, front-end consumption, and customer-facing AI capabilities. We translate business needs into scalable architecture designs that align with enterprise principles, business goals, and technology standards. We evaluate technology choices, platforms, and architectural patterns and recommend secure, scalable, compliant solution components. We lead design reviews and provide architectural direction for high-impact initiatives across data, application, AI, and cloud platforms. We ensure solutions satisfy non-functional expectations for availability, performance, security, observability, compliance, operability, and cost efficiency. We lead EDW integration architecture by defining resilient ELT/ETL patterns, data contracts, lineage, quality checks, governance controls, and service expectations. We model data for analytics using facts, dimensions, semantic layers, and data products that support BI tools, APIs, reporting applications, and AI consumption patterns. We improve performance through tuning, partitioning, clustering, caching, and cost governance across storage, compute, and query layers. We design patterns that enable secure use of structured and unstructured enterprise data in AI solutions through governed RAG pipelines. We define metadata, lineage, governance, and knowledge-management strategies that strengthen trust, retrieval quality, and response grounding. We architect semantic layers, data products, and knowledge graphs that enhance contextual retrieval and reasoning across customer analytics platforms. We define architecture patterns for AI-powered analytics products, including conversational analytics, natural language querying, automated insight generation, intelligent reporting, and autonomous workflow orchestration. We design scalable Agentic AI architectures using LLMs, multi-agent orchestration, tool calling, memory management, enterprise APIs, and secure execution patterns. We establish reference architectures for RAG solutions, including ingestion, chunking, embeddings, vector search, semantic retrieval, prompt orchestration, grounding, and evaluation. We lead integration of enterprise data products with Azure OpenAI, Azure AI Foundry, Azure AI Search, vector databases, and external AI APIs. We define and promote AI governance practices covering responsible AI, model monitoring, prompt safety, privacy, auditability, explainability, and risk management. We establish best practices for prompt engineering, model evaluation, AI observability, retrieval quality measurement, agent testing, and continuous model improvement. We partner with full-stack engineering teams to shape service boundaries, API contracts, integration patterns, and secure data consumption models. We guide engineering teams in building AI services, copilots, intelligent agents, and conversational experiences within customer-facing analytics products. We create architecture decision records, solution diagrams, API specifications, data contracts, standards, and knowledge-sharing materials. We mentor engineers, data engineers, and architects on architecture patterns, secure coding, testing, reliability, logging, metrics, tracing, alerting, incident response, and operational readiness. We drive execution from architecture documents to working reference implementations and reusable production-grade patterns. We partner across product, data governance, security, customer success, engineering, and business teams to convert business outcomes into technical roadmaps. We embed security by design, including authentication, authorization, least privilege, encryption, secrets management, secure APIs, and secure data sharing. We define controls for secure enterprise data use in GenAI applications, including vector stores, embeddings, prompts, LLM interactions, model outputs, and auditability. We support governance for PII/PHI, regulatory obligations, security controls, and audit readiness. We lead architecture reviews, risk assessments, threat modeling, and secure-by-default design reviews for data and AI products. We ensure architecture decisions align with enterprise standards, architecture guidelines, and governance principles.
Technologies:
AI API Architect Azure Cloud Data Warehouse Databricks ETL Support JSON LLM REST SQL Security Snowflake Spark
More:
We are McKesson, an impact-driven Fortune 10 healthcare company that supports nearly every part of the healthcare ecosystem with insights, products, and services that help make care more accessible and affordable. We foster a culture of growth, innovation, and impact, empowering our people to shape the future of health for patients, communities, and one another. In this role, we operate with significant autonomy and partner across enterprise teams to advance customer analytics, enterprise data warehouse, and AI-enabled data products that deliver measurable business value.
last updated 29 week of 2026