Data Product Manager

Level Access is seeking a Data Product Manager to own enterprise data products that power reporting, analytics, operational intelligence, and AI-enabled workflows across the business.

Reporting to the Sr. Director of Business Systems, this role bridges business stakeholders and the Data & AI Engineering team to translate business problems into trusted data products, reusable context, analytics capabilities, and AI-ready solutions.

Success in this role is measured not by data delivered, but by business outcomes enabled.

Responsibilities

Own the Data Product Roadmap

• Partner with business leaders to identify high-value data, analytics, automation, and AI opportunities.
• Translate business objectives into a prioritized roadmap of reusable data products and capabilities.
• Prioritize work based on business impact, operational efficiency, customer outcomes, AI readiness, and strategic goals.
• Define success measures that connect data product delivery to business outcomes.

Define Trusted Data Domains

• Define enterprise data domains, ownership models, business definitions, metrics, KPIs, and data contracts.
• Maintain the enterprise data dictionary and business glossary.
• Drive consistency across reporting, dashboards, operational systems, and AI-enabled workflows.
• Advocate for data quality, lineage, governance, stewardship, and appropriate use of sensitive data.

Drive AI-Augmented Product Development

• Use rapid experimentation, AI-assisted prototyping, and iterative validation to shape data and AI product opportunities.
• Translate business problems, signals, and data opportunities into clear product specifications for Data Engineering, Applied AI Engineering, Business Systems, and other technical teams.
• Use AI tools to accelerate discovery, synthesis, requirements development, prototype generation, documentation, and decision support.
• Help establish a prototype-to-spec workflow that turns validated ideas into actionable work for AI-augmented engineering teams.

Partner with Engineering in an AI-Augmented Delivery Model

• Work as part of a small cross-functional Data & AI Product Team that collaborates early and often across product, data, AI, business systems, and engineering disciplines.
• Partner with Data Engineers and Applied AI Engineers on solution design, prototype review, specification refinement, validation, testing, instrumentation, and adoption planning.
• Ensure technical solutions align with business intent, data requirements, technical feasibility, governance expectations, and measurable outcomes.
• Support an AI-augmented SDLC where specifications, test cases, implementation guidance, and documentation may be accelerated with AI and validated by human expertise.

Qualifications

Required

• 5+ years of experience in Product Management, Data Product Management, Business Systems, Analytics, or a related discipline.
• Experience working with senior business stakeholders to define priorities, requirements, tradeoffs, and success measures.
• Experience supporting AI, machine learning, copilots, agentic workflows, or intelligent automation.
• Strong understanding of data platforms, reporting, BI, analytics, data governance, and enterprise data concepts.
• Experience translating business needs into product specifications, technical requirements, user stories, acceptance criteria, and measurable outcomes.
• Experience working in highly collaborative product and engineering environments that use rapid prototyping, iterative discovery, and early technical partnership.
• Strong communication, facilitation, and stakeholder management skills.

Preferred

• Familiarity with AI-assisted product workflows, rapid prototyping, prompt-based experimentation, or AI-generated specification and documentation approaches.
• Experience with modern data platforms such as Databricks, Snowflake, Microsoft Fabric, or equivalent.
• Exposure to enterprise SaaS business models and operational metrics.

Key Success Measures

• Increased adoption of trusted enterprise data products.
• Faster delivery of reporting, analytics, business insights, and AI-enabled workflows.
• Improved data quality, trust, governance, and reuse across the organization.
• Demonstrable business impact from data and AI investments.