Job description

About Fundraise Up We're Fundraise Up, a global fundraising platform built to make donating to nonprofits fast, seamless, and accessible to all. Every month, our technology powers tens of millions of dollars in donations across the globe. Many of the world's leading nonprofits trust our platform, including UNICEF, the Alzheimer's Association, and a wide range of global NGOs, and we hold a 4.9/5 rating across top software review platforms. Our mission is to unlock the world's generosity potential by optimizing how people give. About the Role AI is only as good as the data underneath it. This role makes ours trustworthy. As our Head of Data, 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 our CFO, which keeps you neutral to any single function and close to how the business actually runs and measures itself. 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 our AI Transformation team, and grow this into a full function as it proves its value. Key Responsibilities Own the data infrastructure, hands-on. Design, build, and run the pipelines and ETL/ELT that feed our warehouse. Own freshness, reliability, and cost. Treat the infrastructure as never finished, and keep making measurable gains in coverage, latency, and reliability. Build the trusted data layer everyone relies on. Own the data models, tests, validation, observability, and lineage that prove the data is accurate, plus the consistent metrics that give two teams the same answer to the same question. Make it self-serve and document it well enough that PMs, analysts, internal builders, and AI agents can answer their own questions. Own the definitions library. Teams own their definitions; you give them a single standardized, versioned home. Set the data contracts for internal AI and programmatic access, including MCP. Decide which data and actions AI agents and internal builders can reach, on what terms, with what semantics, and with what freshness and correctness guarantees. You own the requirements; Engineering builds and runs the servers and APIs. Be the source of truth for business and finance data. What Finance closes on, what Sales forecasts against, and what Product measures should all trace back to the same underlying data. Be the dependable upstream for Product Analytics, Product ML, and AI builders across the company. Work with them on their roadmaps so the foundation stays ahead of what they need. They own their products; you speed them up. Lead as a player-coach. Set the technical bar and review the work of a two-person team, then grow the team and the function as demand and trust build. Ship one major internal AI-powered initiative every quarter. Judge which ideas justify the build, then get them into production. At Fundraise Up, AI is a default tool, not an experimental one. We expect every team member to actively use AI in their day-to-day work, identify where AI can change the shape of problems in their function, and grow their fluency as the tools evolve. Skills and Qualifications You've personally built and run production pipelines/ELT into a cloud data warehouse. You have 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 good enough that questions stopped routing back to you. You've defined the contract between a data platform and the systems that consume it programmatically (schemas, semantics, guarantees, scoped access), and you held engineering partners to it. You've shipped AI/ML to production, not just prototypes, and you can tell which ideas are worth building. You've been the dependable data provider for analytics and ML teams, and you've reconciled revenue, finance, and GTM data so leadership worked from one set of numbers. 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 in terms of what it means for the business, without jargon or condescension. Nice to Have Deeper LLM/agentic experience: orchestration frameworks, evals, RAG Hands-on experience building MCP servers or internal APIs Analytics engineering (dbt) and BI enablement Salesforce and GTM systems data Payments, fintech, or nonprofit-sector exposure You'll Thrive Here If You… Would rather be the person who debugs the broken pipeline than the person who asks who owns it. Measure your success by how rarely people have to double-check the numbers. Want teams to build their own dashboards and AI tools, and see your job as making that safe rather than doing it for them. Think no piece of infrastructure is ever finished and keep looking for the next measurable improvement. Push back candidly when a definition is fuzzy or a request would break trust in the data, and then help fix it. Keep your ego low and your technical bar high. Are excited to build a function from the ground up, not inherit one. Compensation The compensation range for this role is Base Salary: $195K - $213K, Variable 15%. OTE: 224K - 245K Final compensation will be determined based on relevant experience, skills, qualifications, alignment with the role's requirements and location. Perks & Benefits Health, Dental, and Vision insurance covered at 100% for employees, 80% for employee plus dependents, and 70% for employees plus family. FSA and HSA Spending Account. 20 days of vacation, 5 sick days, 11 company holidays plus an additional 1 floating holiday. 401(k) plan with company match. 100% Company-paid short-term disability, long-term disability, basic life insurance and AD&D. Paid parental leave (12 weeks for primary caregivers / 6 weeks for secondary caregivers). Generous home office stipend to support your remote workspace. Annual professional development stipend to support your growth (e.g., workshops, courses, and seminars). Charitable giving program and paid volunteer time off with registered non-profits. ***Please note: All official correspondence from Fundraise Up will exclusively originate from the @fundraiseup.com domain. Exercise caution and ensure the authenticity of emails claiming to be from our company.*** Please note that final compensation will be determined based on relevant experience, skills, qualifications, alignment with the role’s requirements and location. Total Compensation Range $224,000 — $245,000 USD We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, gender, sexual orientation, gender identity or expression, age, national origin, disability, or any other characteristic protected by applicable law in the countries where we operate.

Requirements

Must-haves

AI is only as good as the data underneath it. This role makes ours trustworthy.
Own the definitions library. Teams own their definitions; you give them a single standardized, versioned home.
Be the source of truth for business and finance data. What Finance closes on, what Sales forecasts against, and what Product measures should all trace back to the same underlying data.
Lead as a player-coach. Set the technical bar and review the work of a two-person team, then grow the team and the function as demand and trust build.
Ship one major internal AI-powered initiative every quarter. Judge which ideas justify the build, then get them into production.
Skills and Qualifications
You've personally built and run production pipelines/ELT into a cloud data warehouse. You have 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 good enough that questions stopped routing back to you.
You've defined the contract between a data platform and the systems that consume it programmatically (schemas, semantics, guarantees, scoped access), and you held engineering partners to it.
You've shipped AI/ML to production, not just prototypes, and you can tell which ideas are worth building.
You've been the dependable data provider for analytics and ML teams, and you've reconciled revenue, finance, and GTM data so leadership worked from one set of numbers.
You've built or scaled a data capability from a small base as its founding technical leader.

Nice-to-haves

Deeper LLM/agentic experience: orchestration frameworks, evals, RAG
Hands-on experience building MCP servers or internal APIs
Analytics engineering (dbt) and BI enablement
Salesforce and GTM systems data
Payments, fintech, or nonprofit-sector exposure
You'll Thrive Here If You…
Would rather be the person who debugs the broken pipeline than the person who asks who owns it.
Measure your success by how rarely people have to double-check the numbers.
Want teams to build their own dashboards and AI tools, and see your job as making that safe rather than doing it for them.
Think no piece of infrastructure is ever finished and keep looking for the next measurable improvement.
Push back candidly when a definition is fuzzy or a request would break trust in the data, and then help fix it.
Keep your ego low and your technical bar high.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlsqlpythonllm

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave

Professional Development