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.
This is a builder role, not a consumer role. The right candidate has done this work before: translating messy, distributed enterprise data into trusted, finance-ready outputs, and standing up agentic workflow patterns that hold up under real business conditions. The role sits within Finance and partners closely with Data/Architecture, Enterprise Solutions, and AI Enablement functions across Q2.
RESPONSIBILITIES
Finance Data Engineering & Semantic Modeling
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
Establish and drive adherence of naming standards, data quality checks, refresh cadences, and model documentation as part of a Finance semantic layer contract
AI Workflow Infrastructure & MCP Layer Ownership
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
Author and version markdown-based skills, agent prompts, and workflow files that operationalize recurring Finance tasks — variance narratives, forecast driver updates, close support analyses, executive dashboard refresh, earnings narrative updates, and others as the library grows
Create and maintain a Finance AI artifact library: reusable prompts, golden examples, known failure modes, troubleshooting guidance, and acceptance criteria
Cross-Functional Partnership & Enablement
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
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
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
Exposure to AI evaluation frameworks: prompt quality assessment, hallucination reduction patterns, agent guardrail design, or output validation
This position requires fluent written and oral communication in English.
Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.
Health & Wellness
Hybrid Work Opportunities
Flexible Time Off
Career Development & Mentoring Programs
Health & Wellness Benefits, including competitive health insurance offerings and generous paid parental leave for eligible new parents
Community Volunteering & Company Philanthropy Programs
Employee Peer Recognition Programs – “You Earned it”
Click here to find out more about the benefits we offer.
