Finance Data Architect

Q2•Austin, TX

3d•Hybrid

About The Position

The Finance Data Architect will bridge the gap between complex, multi-system enterprise data and consistent usability for the Finance department. This role involves owning two key areas: building and governing finance-ready semantic models and curated datasets from Q2's data estate, and authoring the AI workflow infrastructure (skills files, agent prompts, MCP context layers, and documentation) to enable Finance to execute complex, recurring processes repeatably and at scale. This is a hands-on builder role focused on translating distributed enterprise data into trusted, finance-ready outputs and establishing agentic workflow patterns. The position sits within Finance and collaborates closely with Data/Architecture, Enterprise Solutions, and AI Enablement functions.

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, 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.
Proven cross-functional influence as an individual contributor — earns trust through technical credibility and clear communication, not organizational authority.
Fluent written and oral communication in English.
Authorized to work for any employer in the U.S.

Nice To Haves

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.

Responsibilities

Map, connect, and rationalize Finance-relevant data across Q2's full data estate, establishing canonical source alignment and lineage documentation.
Design and maintain curated datasets for Finance consumption, including 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.
Establish and drive adherence to naming standards, data quality checks, refresh cadences, and model documentation for the Finance semantic layer.
Build lightweight validation and reconciliation processes to foster trust and adoption among Finance data consumers.
Own the Finance MCP layer, designing and maintaining context, definitions, guardrails, and grounding structures for AI agents.
Author and version markdown-based skills, agent prompts, and workflow files to operationalize recurring Finance tasks.
Create and maintain a Finance AI artifact library, including reusable prompts, examples, failure modes, and troubleshooting guidance.
Establish versioning standards and metadata practices 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, aligning 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.
Identify and surface process improvement and automation opportunities across Finance workflows.
Maintain a flexible mindset to operate with ambiguity while driving teams forward.
Continuously learn and evolve as applied technologies mature.