opengreenhousetechchange
Senior Manager, Data - Fundraising and Marketing (Remote)
GiveDirectly
LocationRemote, Global
WorkplaceFull
Last observed2026-06-13 05:25:32.567023
Job idtechchange-givedirectly:greenhouse:4663672005
GiveDirectly has delivered more than $1B in cash directly to 2+ million people living in poverty across 15 countries since 2011. We believe cash transfers are one of the most scalable, cost-effective, and dignified forms of aid, with the research to back it up. Our work has been covered by The Economist , NPR , TED , and The Washington Post . We are one of Time100’s Most Influential Companies of 2026. Our culture is candid, analytical, and non-hierarchical. We support high ownership and real professional growth. Curious about what it's really like to work here? Read our values and hear from the people who do . If they resonate, this could be a great fit! Location: This role is fully remote but must overlap with an East Africa timezone by at least 3 hours. We are unable to sponsor or take over sponsorship of employment Visas in the U.S. or U.K. at this time. Priority Application Deadline: March 4, 2026 or until we receive a critical mass of applications About this Role GiveDirectly’s Fundraising team is responsible for unlocking significant, sustained funding to scale direct cash transfers globally. As we scale, our fundraising systems must become increasingly rigorous, automated, and compounding. Today, many of our core fundraising metrics and analyses require manual effort; this role will help transform that reality. We are hiring a Senior Manager, Data (Fundraising & Marketing) to serve as the primary strategic data partner to our fundraising and marketing teams. You will combine deep fundraising context with strong technical expertise to define the right questions, establish durable source-of-truth metrics, and build leveraged data products that reduce ad hoc work over time. You will function as a senior individual contributor with significant scope and autonomy. You will directly own significant data modeling and transformation work within the data lakehouse. You will be responsible for building the curated data models that power reporting, forecasting, and decision-making. Our data infrastructure is built on AWS and Databricks, with dashboards and visualizations in Tableau. You will primarily use SQL to query data and contribute to data pipelines, and Python notebooks for deeper analysis. This is a full-time, globally remote position with travel 2–3 times per year for team retreats or planning sessions. Core Responsibilities Data Product and Metrics Ownership Set vision and priorities for how data supports fundraising and marketing strategy, based on a deep understanding of GiveDirectly’s fundraising model, donor lifecycle, and growth strategy, across revenue streams Define, standardize, and document core fundraising and marketing KPIs (e.g., retention, LTV, revenue forecasting metrics) to drive fundraising goals Design and build robust data models in the warehouse that serve as the source of truth for fundraising reporting - weekly, monthly, quarterly and annually Establish clear, trusted source-of-truth datasets and metric logic used consistently across Fundraising, Finance, and Leadership, continually folding learnings in to update the metrics and definitions Identify recurring analytical needs and convert them into scalable, automated data products Scope and prioritize fundraising data products in partnership with Fundraising and Marketing Data stakeholders Partner with data engineering on upstream data quality and pipeline improvements, while owning the downstream analytical layer Continuously reduce ad hoc reporting by investing in durable systems that compound in value over time Strategic Fundraising and Marketing Analytics Serve as the primary data partner to fundraising and marketing leadership, informing strategy across acquisition, retention, and revenue expansion Build and maintain revenue forecasts and donor cohort models to guide annual planning and budget allocation Support channel investment decisions with clear ROI frameworks, experiment design, and performance analysis Proactively surface decision-releva
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