Enterprise AI and Data Architect

About the opportunity You’ll be the enterprise architect for data and AI across all seven of our business units. You’ll set the architecture, standards and shared platform that every team builds on, so business units can focus on building products that set them apart, within clear guardrails for security, governance and interoperability. A big part of the job is drawing the line between what we build once centrally (the shared foundation, platform services and standards) and what each business unit owns and builds for itself. You’ll be accountable for making sure our AI and data investments deliver measurable business results and a lasting competitive advantage. This is a hands-on role. You’ll spend 60 to 80% of your time building. What you’ll do Architecture and standards Own the enterprise target architecture for data and AI, and publish and maintain reference architectures, standards and decision records Set architectural principles: modularity, API-first design, interoperability, reuse, portability and lifecycle management Decide what’s central by default, what becomes central once reuse is proven, what stays with business units, and what’s jointly owned Give clear guidance on which technologies, models and patterns to use for which job Keep a forward-looking view of AI technologies, vendors, models and frameworks Agentic AI platform Help shape the reference architecture for model access, context management, orchestration, retrieval, evaluations, guardrails, identity and telemetry Help design multi-tenancy patterns that scale to 130,000+ businesses, including agents built by customers and partners Set release requirements for agents: approvals, versioning, rollback, guardrails as policy code and evaluation gates Data architecture Set enterprise data standards on Snowflake: semantic layer, governed metrics, data contracts, quality rules, lineage, retention and deletion Define the split between central standards and business-unit domain models, and review those models for consistency Define the technical criteria for data readiness used in product planning Work with Legal and Privacy on the data architecture for our industry insights product, covering rights, aggregation, consent and lineage Standards for AI-assisted development Turn standards into context that AI coding agents use: repository instruction files, reusable skills and prompts, MCP tool definitions and starter templates Build architecture rules into CI: policy as code, fitness functions,...

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