Q2
digital banking and lending solutions
FinanceDataArchitect
Neural analysis suggests this role is
optimal for Mid candidates.
“Finance Data Architect at Q2. Skills: Building and governing finance-ready semantic models, Authoring AI workflow infrastructure, Translating messy, distributed enterprise data into trusted, finance-ready outputs, Standing up agentic workflow patterns. Map, connect, and rationalize Finance-relevant data across Q2's full data estate. Design and maintain curated datasets purpose-built for Finance consumption”
What You'll Achieve.
Building and governing finance-ready semantic models and curated datasets; Authoring the AI workflow infrastructure; Allows Finance to execute complex, recurring processes repeatably and at scale; Translating messy, distributed enterprise data into trusted, finance-ready outputs; Standing up agentic workflow patterns that hold up under real business conditions; Drive trust and adoption across Finance data consumers; Ensure agents and tools are grounded in approved semantic definitions and operate within Finance governance guardrails; Drive adoption through documentation, demos, and stakeholder enablement; Bring forward use cases grounded in data and feasibility
Industry & Context.
Rationalize distributed enterprise data; Identify and surface process improvement and automation opportunities
Applicants must be authorized to work for any employer in the U. S., Unable to sponsor or take over sponsorship of an employment Visa
What They're Looking For.
Must Have
Bachelor’s degree in Finance, Accounting, Analytics, Information Systems, or related field plus 5–7 years of relevant experience, Proven ability to navigate and rationalize distributed enterprise data environments, 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, Demonstrated ability to design and structure AI workflow infrastructure, Fluent written and oral communication in English, Authorized to work for any employer in the U. S.
Nice to Have
Advanced degree with 3–5 years of equivalent demonstrated experience, 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), 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
What You'll Do.
and rationalize Finance-relevant data across Q2's full data estate
Design and maintain curated datasets purpose-built for Finance consumption
and FinOps stakeholders to define semantic models
Establish and drive adherence of naming standards
and model documentation
Build lightweight validation and reconciliation processes
Own the Finance MCP layer: design and maintain the context
and grounding structures
Author and version markdown-based skills
Create and maintain a Finance AI artifact library
Establish versioning standards and metadata practices for all Finance AI artifacts
How You'll Work.
Team & Collaboration
Partner closely with Data/Architecture, Enterprise Solutions, and AI Enablement functions; Partner with FP&A, Accounting, and FinOps stakeholders; Serve as the connective layer between Finance and Q2's enterprise data; Align with Data/Architecture and Enterprise Solutions on upstream transformations, governance standards, and canonical source decisions; Drive adoption through documentation, demos, and stakeholder enablement; Collaborate with other local companies and community organizations
Communication Scope
Exceptional written communication; Clear communication; Translating technical outputs into Finance-accessible language
Full Job Description
# **As passionate about our people as we are about our mission.** **_Why Join Q2?_** Q2 is a leading provider of digital banking and lending solutions to banks, credit unions, alternative finance companies, and fintechs in the U.S. and internationally. Our mission is simple: build strong and diverse communities through innovative financial technology—and we do that by empowering our people to help create success for our customers. **_What Makes Q2 Special?_** Being as passionate about our people as we are about our mission. We celebrate our employees in many ways, including our “Circle of Awesomeness” award ceremony and day of employee celebration among others! We invest in the growth and development of our team members through ongoing learning opportunities, mentorship programs, internal mobility, and meaningful leadership relationships. We also know that nothing builds trust and collaboration like having fun. We hold an annual Dodgeball for Charity event at our Q2 Stadium in Austin, inviting other local companies to play, and community organizations we support to raise money and awareness together. **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 Fi
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