Data Product Manager, Finance
MongoDB•Austin; New York City; Palo Alto; San Francisco; United States
Full-timeMid Level
👁️ 3 views•📝 0 applications•Posted 5/13/2026•Expires 8/23/2026
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Job Description
The Internal Data team powers decisions and applications across MongoDB, organized around four core business areas: GTM, Product & Technology, Finance, and HR. We build and scale the data products MongoDB employees rely on every day, including data pipelines, datasets, dashboards, APIs, ML models, and frameworks that form the backbone of our internal data ecosystem. Our work directly enables teams to hit their KPIs, automate workflows, and build the AI and BI capabilities that drive the business forward. We treat data as a product. That means applying the same rigor, user focus, and outcome orientation to internal data that MongoDB brings to its external platform. As the company continues to grow, the scope and stakes of that work grow with it. This role is focused on our Finance domain, where data products support smarter planning, forecasting, spend efficiency, and financial performance analysis. If you want to build data products that matter at scale, this is the role for you. This role can be based out of our NYC, Austin, Palo Alto, or San Francisco office locations or remotely in the United States. What You'll Do In this role you will own Finance-focused data products end to end, with cross-functional teams to set priorities, shape requirements, and drive delivery. You will build and maintain pipelines, datasets, dashboards, APIs, and frameworks that Finance teams rely on daily for planning, forecasting, and performance analysis, while also contributing to the underlying data management tools and services that power AI and BI across the company. A significant part of the work is understanding your stakeholders. You will spend time with Finance teams learning their workflows, translating their needs into clear requirements and user stories, and building relationships across Finance, engineering, and other technical counterparts. You will keep the voice of the user central in every prioritization and discovery co