What you'll do

You'll work side-by-side with Finance subject matter experts to understand how work gets done, identify the highest impact opportunities, and partner with domain teams to chart a transformation plan which balances future ambitions with rapid impact. You'll work independently to automate manual processes, design and build agents and required data solutions using internal platforms, and enable Finance teams to maintain these solutions long-term.

You'll build with your partner teams, not simply deliver solutions to them. You'll help colleagues move from a manual workflow, to a first AI-enabled win, to a reliable solution they can confidently operate and improve themselves. Success means the workflow stays transformed after the initial build, the team trusts the output, and the people closest to the process can own what comes next.

You'll favor learning through building. When possible, you'll put a useful prototype in front of a Finance user, test it against a real deliverable, and improve it quickly rather than spend weeks describing a theoretical future state. When you encounter platform limitations-which you will-you'll find creative, supportable approaches, collaborate with Engineering to scope and test custom tools, and escalate strategically when needed.

This role requires a blend of process automation, data operations, technical problem-solving, and stakeholder enablement. You'll spend your time building agents, optimizing data pipelines that feed them, creating knowledge layers that make them more accurate, and training Finance teams to use and maintain them. Success means Finance teams can independently run agents you've built while you provide light support and focus on the next automation opportunity.

Responsibilities

• Embed with Finance teams to diagnose workflows, identify high-leverage opportunities, and translate business, data, and control requirements into practical AI and automation solutions.
• Build and operationalize AI agents for Finance use cases, delivering end-to-end solutions from prototype through validation, monitoring, documentation, and handoff.
• Write and optimize SQL for data extraction, transformation, calculation, and validation, ensuring Finance-facing outputs are accurate, explainable, and reliable.
• Design reusable knowledge layers, evaluation methods, validation patterns, data-quality frameworks, and components that improve agent accuracy and accelerate future use cases.
• Identify agentic limitations and new possibilities; determine when to use existing capabilities, develop an alternative approach, or partner with Engineering to scope and test custom tools and integrations, including tools using Model Context Protocol where appropriate.
• Build alongside users, gather feedback through real deliverables, and iterate until the solution fits the workflow and earns user trust.
• Enable long-term ownership through clear SOPs, runbooks, training, and self-service tooling; coach Finance teams to operate, maintain, and evolve what has been built.
• Establish monitoring and observability for deployed agents, including metrics, alerting, and incident-response processes that support reliable operation.
• Diagnose technical and data issues, resolve problems independently where possible, and collaborate with Engineering when solutions require deeper platform changes.
• Measure adoption and impact, share lessons and wins, and turn successful implementations into standards, components, and playbooks for the broader Finance AI portfolio.

Who you are

We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• 5+ years of experience in data analytics, technical operations, business intelligence, automation, solutions delivery, or a related field.
• Hands-on experience building AI-enabled tools, agents, automations, or workflows that changed a real business process-not solely using AI as a conversational tool.
• Strong SQL proficiency, including experience writing complex queries using CTEs, window functions, and joins for data analysis, transformation, or pipeline logic.
• A track record of independently scoping and delivering technical solutions for process improvement, demonstrating ownership from problem definition through adoption.
• Strong analytical and investigative skills, including the ability to identify root causes, debug complex data problems, and resolve inconsistencies.
• Strong written and verbal communication skills, with the ability to explain technical concepts to non-technical audiences and work effectively with domain experts and Engineering partners.
• Experience teaching, coaching, enabling users, or transferring ownership of a solution through documentation, training, or self-service tools.
• Working knowledge of development practices such as version control, testing, code review, and iterative delivery.
• Experience using low-code tools, scripting, workflow automation, AI-assisted development, or custom integrations, paired with curiosity and the ability to learn evolving technologies.

You will also likely have (not required):

• Domain experience in Finance Operations, Financial Planning & Analysis (FP&A), Accounting, Treasury, Tax, Payments, or other areas.
• Familiarity with modern data tools and practices, such as Python, Databricks, ETL/ELT processes, data pipelines, and data-quality controls.
• Experience with Model Context Protocol or another extensibility or integration framework.
• Experience navigating financial systems and understanding how data flows through accounting, reporting, reconciliation, forecasting, or payments processes.
• Experience with change management, organizational transformation, or large-scale enablement programs.
• Experience building internal tools, templates, components, or playbooks that were adopted beyond the original team or use case.
• Experience working in a high-growth technology company with rapidly evolving processes and tools.

Note on technical depth: This role involves working at the intersection of no-code AI tools and custom development. While software engineering expertise is not required, you should be comfortable applying technical concepts, collaborating closely with Engineering teams, and using AI-assisted development tools to extend platform capabilities when needed.