Lead Data Product Manager

We're looking for a Lead Data Product Manager to own the data products that power how we run the business. This is a greenfield opportunity: you'll build the foundations, earn stakeholder trust, and ship data products people actually use to make decisions. You'll partner directly with Finance and People & Culture leaders to translate their priorities into well-scoped data products, working alongside Analytics and Data Engineering to bring them to life. You own requirements, quality, and adoption — not just delivery. Every data product we build must support multiple consumers: a Finance analyst in a BI dashboard, an AI agent answering a business question, or a downstream application. Consistent definitions, reliable outputs, and documented semantics are the standard. You’ll also use AI in your own workflow to improve how you synthesize requirements, analyze stakeholder input, and accelerate product development. This role reports to the VP, Data & Analytics. What You'll Do Define and Own the Data Product Roadmap Partner with Finance and P&C leaders to identify data gaps, prioritize opportunities, and build a roadmap that delivers measurable value Design data products that support multiple consumption patterns, including BI tools, AI-enabled experiences, and business applications Use data usage patterns — dashboard activity, query volume, and recurring reporting requests — to proactively identify unmet needs Translate business objectives into clear requirements, challenging assumptions early to ensure teams are solving the right problems Drive Cross-Functional Alignment Serve as a strategic data partner across business stakeholders, Analytics, and Data Engineering teams Facilitate collaboration across functions while identifying shared data needs and reducing siloed solutions Build prioritization frameworks that balance competing needs and provide transparency around trade-offs Communicate effectively with technical teams, business leaders, and executives Lead Data Product Development Own the full product lifecycle across multiple Finance and P&C initiatives, from discovery through Alpha, Beta, and GA releases Design user-focused data experiences that balance technical requirements with stakeholder needs Define and maintain semantic contracts, including field definitions, metric logic, and data grain specifications Identify reusable data assets and scalable models that support multiple downstream use cases Maintain a high bar for accuracy, freshness, and reliability while proactively resolving issues before they impact users or AI workflows Use AI tooling to improve product workflows, including requirements synthesis, AI-assisted SQL validation, catalog generation, and data pattern analysis Drive Adoption and Measure Impact Define success metrics for each data product and measure adoption and business impact over time Develop a deep understanding of end-user workflows and leverage personas to create solutions that address real business needs Gather stakeholder feedback after launch and continuously improve products based on usage and outcomes Ensure data products are structured and documented to support trusted AI-enabled experiences Build in Partnership with Engineering Serve as the product counterpart to Data and Analytics Engineering — owning the what and why while partnering on the how Collaborate on semantic layer design and dbt transformations to create consistent metric definitions across BI tools, LLMs, reverse ETL pipelines, and applications Identify opportunities to improve ingestion, transformation, and delivery processes while advocating for investments that increase reliability and scalability Lead Agile delivery cycles by translating roadmap priorities into Initiatives and Epics with clear acceptance criteria Enforce Data Governance & Quality Lead data catalog efforts for your product area, including discoverability, lineage tracking, and documentation Champion data quality frameworks and governance standards around access controls, retention, and responsible data usage Partner across teams to solve systemic data challenges rather than shifting complexity downstream Foster a Data-Driven Culture Present compelling data stories that help technical and non-technical stakeholders make informed decisions Develop enablement resources, including workshops, documentation, and training, to increase adoption of data products Build AI literacy across stakeholder communities by helping teams understand how to effectively use AI tools grounded in trusted data Contribute reusable components, patterns, and best practices that help the broader Data & Analytics organization accelerate delivery What You'll Need Minimum Qualifications 6+ years in product management, with 3+ years focused on data products, analytics platforms, or data infrastructure Demonstrated experience building data products in a Finance, HR, Legal, operations, or similar business domain Bachelor’s degree in Engineering, Computer Science, Data Science, Business, or equivalent experience Preferred Qualifications Proven ability to operate in greenfield or early-stage data environments where you’ve created structure while delivering results Strong understanding of data pipelines, dimensional modeling, semantic layers, and modern analytics stacks (dbt, Snowflake, BI platforms) Solid understanding of data governance principles, including metadata management, lineage, quality frameworks, and access controls Proficiency in SQL with the ability to independently query and validate data Exceptional stakeholder management skills across senior business and technical audiences Background in SaaS or product-led growth companies Experience with data catalog platforms (e.g., Atlan, Alation) Familiarity with cloud and data tooling: Cloud: AWS, Snowflake Transformation & orchestration: dbt, Fivetran, Airflow V