Senior Product Manager - Data Platform and Integrity

Forward Financing•Boston, MA

1d•$145,000 - $180,000•Remote

About The Position

Forward Financing is a financial technology company based in Boston, Massachusetts with team members throughout the United States, Dominican Republic, and Canada. The company is on a mission to unlock the capital that fuels small businesses across America. Recognized as a Best Place to Work by Built In Boston and certified as a Great Place To Work®, Forward is investing in its employees, technology, and customer experience – with long-term success in mind every step of the way. We are a rapidly growing fintech company that believes the strategic use of data is the key to achieving this goal. We are seeking a technical and collaborative Senior Technical Product Manager, Data Platform & Integrity to serve as a horizontal leader within our Product and organization. You will partner across all Product Squads to ensure that our Core Applications (Salesforce, Core Apps, Data Warehouse) and proprietary tools are built with a "Data-First" mindset. You will be the guardian of our data model, the bridge between Engineering and Analytics, and the strategic partner ensuring that every PRD includes a robust data strategy. You will report directly to the Vice President of Product.

Requirements

5+ years of strategic product management experience in FinTech, lending, or a regulated financial services environment.
2+ years of direct, dedicated experience establishing or leading a formal Data Governance program, Data Stewardship practices, or Master Data Management (MDM) strategy.
Expertise in data quality assurance, data lineage, and metadata management, with experience implementing data catalogs or data dictionary tools.
Solid understanding of lending data lifecycles (e.g., application, servicing, collections) and the compliance requirements (TCPA, FDCPA, etc.) inherent in revenue-based financing or related credit products.
Demonstrated experience with API integrations and managing complex, high-volume data flows between disparate systems.
Strong analytical skills with a proven track record of using data and governance principles to define product strategy and measure results.
Bachelor’s degree in Business, Finance, Computer Science, or a related quantitative field.

Responsibilities

Product Data Strategy & PRD Governance (Horizontal Partnership)
Embed Data Strategy: Partner with vertical Product Managers and Engineering leads during the design phase of new features. You will ensure that every Product Requirement Document (PRD) includes a defined data strategy—specifying exactly how data is captured, stored, and validated.
Design Review: Act as a technical reviewer for major product initiatives, asking the critical questions: “How does this change the data model? Does this create a duplicate field in core applications? How will we report on this?”
Standardization: Establish the standards for how our Product team defines data requirements, ensuring consistency in how we track loan lifecycles, customer statuses, and financial transactions across different squads.
Core Application & Salesforce Data Integrity
Data Modeling: Oversee the logical data model within our Core Applications and Salesforce. You will ensure that as we scale, our schema remains clean, normalized, and efficient, preventing "tech debt" in the form of messy or redundant data fields.
Lineage & Overrides: Own the logic for Data Lineage and Data Overrides. You will define the rules for which system is the "source of truth" when data conflicts occur and ensure we have an audit trail for manual overrides in core apps.
Governance at the Source: Implement "Governance by Design" by working with Engineering to build validation rules and constraints directly into the application layer, preventing bad data from ever entering our ecosystem.
Strategic Analytics Alignment
The Analytics Bridge: Serve as the primary liaison between the Product/Engineering organization and the Analytics/Data Science teams. You will ensure that upstream application changes do not break downstream reporting or AI models.
Translation: Translate complex analytical requirements (e.g., specific features needed for a risk model) into actionable engineering tickets for the Core Application teams.
Data Availability: Ensure that the data generated by our products is accessible, documented, and structured in a way that allows the Analytics team to self-serve, reducing the dependency on ad-hoc engineering support.