Q2 Stadium Jobs (NOW HIRING)

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 anddrive adherence ofnaming 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 ambiguitywhile continuing todrive 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 5-7 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 structureAI workflow infrastructure:includingbuilding prompt libraries, authoring agent skills or context files, or structuring MCP / retrieval-grounding layersOR 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

Preferred

• 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