Fundraise Up is a global fundraising platform built to make donating to nonprofits fast, seamless, and accessible to all.
Every month, the technology powers tens of millions of dollars in donations across the globe.
Trusted by leading nonprofits including UNICEF and the Alzheimer's Association.
About the role
AI is only as good as the data underneath it. This role makes ours trustworthy.
You'll build and guarantee the data foundation the whole company builds on: the pipelines, the models, the definitions, and the contracts that let people and AI agents trust what they're looking at.
You'll report to the CFO.
This is a player-coach seat: you'll write code and do the hardest technical work yourself, ship a major internal AI-powered initiative every quarter alongside the AI Transformation team, and grow this into a full function.
Responsibilities
Own the data infrastructure, hands-on: design, build, and run the pipelines and ETL/ELT that feed the warehouse. Own freshness, reliability, and cost.
Build the trusted data layer everyone relies on: data models, tests, validation, observability, and lineage, plus consistent metrics. Make it self-serve and well documented.
Own the definitions library: a single standardized, versioned home.
Set the data contracts for internal AI and programmatic access, including MCP.
Be the source of truth for business and finance data.
Be the dependable upstream for Product Analytics, Product ML, and AI builders across the company.
Lead as a player-coach: set the technical bar and review the work of a two-person team, then grow the team.
Ship one major internal AI-powered initiative every quarter.
Requirements
You've personally built and run production pipelines/ELT into a cloud data warehouse.
Strong SQL and Python, orchestration, and warehouse modeling skills, and you still want to be hands-on.
You've built a trusted, self-serve data layer that people actually relied on: tests, observability, lineage, a metrics or semantic layer, and documentation.
You've defined the contract between a data platform and the systems that consume it programmatically (schemas, semantics, guarantees, scoped access).
You've shipped AI/ML to production, not just prototypes.
You've been the dependable data provider for analytics and ML teams, and reconciled revenue, finance, and GTM data.
You've built or scaled a data capability from a small base as its founding technical leader.
You can explain a modeling decision to non-technical executives.