Data Product Manager

Data Product Manager

Company: YipitData Location: Remote (USA) Contract: Permanent Seniority: Mid-level

About the Role

YipitData is embarking on an ambitious product initiative to build an AI-powered product that revolutionizes how clients interact with data. This role is central to our product strategy, offering a unique opportunity to shape a new way for customers to access and derive value from our data.

As a Data Product Manager, you will be instrumental in bringing this vision to life. You will own the entire process from raw alternative data to trusted, product-ready intelligence, defining how complex datasets are structured, interpreted, and presented to customers. This position sits at the critical intersection of data, product, and AI. You will work with diverse alternative datasets, develop methodologies to transform raw signals into reliable business insights, and serve as a key authority on the product's data utilization. Your expertise will guide decisions on methodological soundness, answerable questions, and necessary guardrails.

Success in this role demands both analytical rigor and a proactive, builder's mindset. You should thrive in ambiguous environments, tackle challenges without established playbooks, and contribute to the future of a highly strategic YipitData product. Your work will directly impact how hundreds of customers engage with our data and will be crucial for the product's long-term scalability.

Key Responsibilities

Data Source Ownership & Methodology Design

• Translate raw alternative datasets into scalable, AI-ready data products.
• Design methodologies to answer high-value business questions, determining how disparate datasets should be combined, normalized, and interpreted.
• Collaborate closely with Data Engineering to refine 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, ensuring these nuances are reflected in downstream outputs.

AI Product Intelligence & Knowledge Systems

• Define how the product should leverage different datasets, including valid query patterns, edge cases, failure modes, and methodological guardrails.
• Own metric definitions, data lineage, and documentation to ensure the product consistently delivers accurate and explainable answers.
• Establish standards for how the product reasons across multiple datasets, preventing over-interpretation and ensuring conclusions are statistically defensible.
• Serve as the final reviewer for methodology-related changes impacting product behavior.

Product Development & Customer Problem Solving

• Translate customer questions into scalable methodologies, data models, and product capabilities.
• Expand the product's ability to answer questions 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 enhance the AI product's commercial value.
• Shape the product roadmap by transforming emerging customer needs and experimental insights into repeatable product functionality.

Data Quality & Operational Excellence

• Coordinate testing and validation of staged data changes before they are deployed to production.
• Manage incident response processes for data quality issues, methodology changes, and upstream source disruptions.
• Build and maintain a library of quality checks specifically tailored to the unique requirements of AI-powered customer experiences.
• Ensure the product 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 proficiency in SQL; comfortable working with data pipelines, schema changes, and understanding upstream/downstream data dependencies.
• Proven experience owning data documentation, metric definitions, or data quality programs, beyond ad hoc analysis.
• Demonstrated ability in cross-functional coordination, particularly between technical data teams and product or commercial stakeholders.
• Strong project management skills, including the ability to manage triage processes, maintain quality libraries, and coordinate across multiple stakeholder groups effectively.
• Clear, structured communication skills, capable of translating complex data methodology questions into actionable guidance for non-technical stakeholders.
• A demonstrable track record of building and shipping products, solving complex problems, and making a significant impact on the products you've worked on.
• An entrepreneurial mindset: comfortable with ambiguity, energized by new problem spaces, and capable of making progress without a fully defined path.
• Deep experience with alternative data, panel data, or similarly complex, nuanced data sources is essential. You must understand the quirks, limitations, and methodological subtleties of these datasets and be able to encode this understanding for an AI-driven product.
• Prior experience with AI/ML products, LLM-based agents, or evaluation frameworks is a strong advantage.

What We Offer

• Competitive base salary with comprehensive benefits.
• Fully remote work environment within the United