DataProductManager

## Accountabilities Own the transformation of raw alternative datasets into structured, scalable, AI-ready data products, defining how data is modeled, interpreted, and delivered to end users. Design and maintain methodologies that translate complex datasets into reliable business insights, including segmentation, cohort analysis, and cross-dataset reasoning. Partner with Data Engineering to ensure robust pipelines, clear definitions, and well-documented data structures that support product scalability. Define and enforce data quality standards, metric definitions, lineage tracking, and methodological guardrails to ensure accuracy and consistency. Establish rules for how the product reasons across datasets, including edge cases, limitations, and acceptable inference boundaries for AI-generated insights. Lead validation, testing, and incident management processes for data changes, ensuring reliability before production deployment. Collaborate with Product and Leadership teams to identify new use cases, improve capabilities, and translate customer needs into scalable data solutions. Requirements: 3–6+ years of experience in data product management, analytics, data science, product analytics, or related data-centric roles. Strong SQL proficiency and solid understanding of data pipelines, schemas, and upstream/downstream dependencies. Experience owning metric definitions, data documentation, or data quality frameworks in production environments. Background working with alternative data, panel data, or similarly complex datasets with methodological nuance and ambiguity. Proven ability to work cross-functionally between technical data teams and product or business stakeholders. Strong communication skills with the ability to simplify complex data methodologies into clear, actionable guidance. Demonstrated ability to build and ship data-driven products or frameworks in fast-moving environments. Comfortable working in ambiguity with an entrepreneurial, builder-oriented mind