Finance Data Architect

External

Q2ebanking · Austin, TX

ContractHybridToday

DocumentationForecastingLeadershipProcess ImprovementSnowflake

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About the role

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

Build lightweight validation and reconciliation processes that drive trust and adoption across Finance data consumers

Build trust through auditability of modeled data

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

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

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

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.

EXPERIENCE AND KNOWLEDGE

Required

Bachelor's degree in Finance , Accounting, Analytics, Information Systems, or related field plus 5-

Finance Data Architect

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