Data Product Manager – Super Agent

Role Overview

• Own the translation of raw alternative datasets into scalable, agent-ready data products.
• Design methodologies that answer high-value business questions, determining how disparate datasets should be combined, normalized, and interpreted.
• Partner closely with Data Engineering to shape source data pipelines into clean, well-structured datasets with clear definitions and documentation.
• Develop deep expertise in the strengths, limitations, biases, and coverage characteristics of key datasets and ensure those nuances are reflected in downstream outputs.
• Define how the Insight Agent should use different datasets, including valid query patterns, edge cases, failure modes, and methodological guardrails.
• Own metric definitions, data lineage, and documentation to ensure the agent consistently delivers accurate and explainable answers.
• Establish standards for how the agent reasons across multiple datasets, preventing over-interpretation and ensuring conclusions remain statistically defensible.
• Serve as the final reviewer for methodology-related changes that impact agent behavior.
• Translate customer questions into scalable methodologies, data models, and agent capabilities.
• Expand the range of questions the agent can answer by enabling new forms of segmentation, cohort analysis, behavioral measurement, and cross-dataset insights.
• Partner with Product, Engineering, and Leadership to identify new data sources, use cases, and capabilities that increase the commercial value of the Insight Agent.
• Help shape the product roadmap by turning emerging customer needs and experimental insights into repeatable product functionality.
• Coordinate testing and validation of staged data changes before they reach production.
• Own incident management processes for data quality issues, methodology changes, and upstream source disruptions.
• Build and maintain a library of quality checks tailored to the unique requirements of AI-powered customer experiences.
• Ensure the agent consistently surfaces reliable, accurate, and internally consistent information across all supported use cases.

Requirements

• 3–6 years of experience in data product management, product analytics, analytics engineering, data science, market intelligence, alternative data, or a closely related field.
• Strong fluency in SQL; comfort with data pipelines, schema changes, and upstream/downstream data dependencies.
• Experience owning data documentation, metric definitions, or data quality programs—not just conducting ad hoc analysis.
• A track record of cross-functional coordination, ideally between technical data teams and product or commercial stakeholders.
• Strong project management instincts: you can run a triage process, maintain a quality library, and coordinate across multiple stakeholder groups without dropping balls.
• Clear, structured communication—you can translate complex data methodology questions into guidance that non-technical stakeholders can act on.
• A demonstrable track record of building—shipping things, solving hard problems, and leaving a clear mark on the products you’ve worked on.
• An entrepreneurial mindset: you’re comfortable with ambiguity, energized by new problem spaces, and don’t need a fully paved road to make progress.
• Deep experience with alternative data, panel data, or similarly complex, nuanced data sources is required—you need to understand the quirks, limitations, and methodological subtleties of these datasets and be able to encode that understanding for an AI agent.
• Prior experience in or exposure to AI/ML products, LLM-based agents, or evaluation frameworks is a strong plus.

Tech Stack

• SQL

Benefits

• Competitive base salary with comprehensive benefits
• Fully remote-friendly within the United States
• Flexible work hours and flexible vacation
• Generous 401(k) match, parental leave, wellness budget, and learning reimbursement
• A growth-oriented environment where advancement is driven by impact—not tenure