Finance Data Architect

About the role

Summary Finance at Q2 operates on enterprise data that lives across a complex, multi-system landscape — Snowflake and beyond. This role exists because that data is not yet consistently usable. The Finance Data Architect closes that gap by owning two interconnected capabilities: building and governing finance-ready semantic models and curated datasets drawn from Q2's full data estate, and authoring the AI workflow infrastructure — skills files, agent prompts, MCP context layers, and documentation — that allows Finance to execute complex, recurring processes repeatably and at scale.

Responsibilities

• Map, connect, and rationalize Finance-relevant data across Q2's full data estate — Snowflake and distributed upstream sources — establishing canonical source alignment and lineage documentation for each Finance domain
• Design and maintain curated datasets purpose-built for Finance consumption: expense forecasting inputs, revenue and COGS drivers, headcount and compensation, and other key reporting and planning inputs
• Partner with FP&A, Accounting, and FinOps stakeholders to define semantic models that encode metric definitions, dimensionality, calculation logic, and source-of-truth alignment in a form downstream systems and AI agents can reliably consume
• Build and maintain lightweight validation and reconciliation processes that drive trust and adoption across Finance data consumers
• Build trust through auditability of modeled data
• Own the Finance MCP layer: design and maintain the context, definitions, guardrails, and grounding structures that enable AI agents to operate accurately within Finance workflows
• Create and maintain a Finance AI artifact library: reusable prompts, golden examples, known failure modes, troubleshooting guidance, and acceptance criteria
• Establish versioning standards and metadata practices (ownership, approval status, context dependencies) for all Finance AI artifacts
• Partner with enterprise AI Enablement teams to ensure agents and tools are grounded in approved semantic definitions and operate within Finance governance guardrails
• Serve as the connective layer between Finance and Q2's enterprise data ecosystem; align with Data/Architecture and Enterprise Solutions on upstream transformations, governance standards, and canonical source decisions
• Drive adoption through documentation, demos, and stakeholder enablement — translating technical outputs into Finance-accessible language and practice
• Identify and surface process improvement and automation opportunities across Finance workflows, bringing forward use cases grounded in data and feasibility
• Flexible mindset to operate with ambiguity while continuing to drive teams forward
• Continuously learn and evolve as applied technologies mature and new technologies arise

Requirements

• Bachelor’s degree in Finance, Accounting, Analytics, Information Systems, or related field plus 5–7 years of relevant experience; advanced degree with 3–5 years; or equivalent demonstrated experience
• Proven ability to navigate and rationalize distributed enterprise data environments — not just Snowflake-native work, but connecting and harmonizing data across multiple source systems
• Strong SQL capability and hands-on experience working in Snowflake or equivalent cloud data warehouse environments
• Demonstrated experience building semantic models, curated datasets, or data layer contracts that translate raw enterprise data into business-facing outputs
• Demonstrated ability to design and structure AI workflow infrastructure: including building prompt libraries, authoring agent skills or context files, or structuring MCP / retrieval-grounding layers OR a proven track record of rapidly acquiring and applying emerging technical capabilities in a production environment
• Exceptional written communication and documentation skills, including the ability to write for both technical and non-technical audiences
• Prominent cross-functional influence as an individual contributor — earns trust through technical credibility and clear communication, not organizational authority
• Finance domain depth in FP&A, expense forecasting, or revenue modeling in a SaaS or public-company environment
• Familiarity with enterprise planning and reporting tools (Anaplan, Power BI, Tableau) and experience designing semantic layers that feed them accurately
• Experience building internal documentation systems, playbooks, or knowledge bases in a markdown-first environment
• Exposure to AI evaluation frameworks: prompt quality assessment, hallucination reduction patterns, agent guardrail design, or output validation
• Comfort operating in an environment where the tooling is established but the patterns are still being built — a builder’s orientation, not an implementer’s

Qualifications

• Fluent written and oral communication in English
• Authorized to work for any employer in the U.S.