Data Product Manager

Responsibilities

• Define and own the long-term vision and roadmap for analytics datasets, governed metrics, and dashboards
• Translate business questions into durable data products (canonical datasets, semantic layers, standardized dashboards)
• Establish and manage a prioritized intake process for analytics requests, balancing quick wins with foundational investments
• Write clear requirements for datasets and dashboards, including metric definitions, grain, dimensions, filters, and refresh expectations
• Partner with Data Engineering to shape data models, pipelines, and data contracts that enable scalable analytics
• Possess a deep understanding of BI/dashboarding ecosystems (e.g., Looker, Tableau, Power BI) and building scalable dashboard suites
• Have a solid grasp of semantic layers or metric layers and the concept of governed, reusable metric definitions
• Execute and manage the operating cadence for execution, including Jira epics/stories, sprint planning, prioritization, and release communication
• Maintain a team wiki (e.g., Confluence) with documentation for datasets, dashboards, metric definitions, and usage guidelines
• Drive quarterly planning, resourcing conversations, milestone tracking, and stakeholder reviews of roadmap progress
• Define and track success metrics for data products, including adoption, data quality, freshness, reliability, and stakeholder satisfaction
• Create clear documentation and knowledge management systems in a wiki environment (e.g., Confluence)
• Drive adoption through enablement, training, office hours, and stakeholder feedback loops

Requirements

• Strong product management skills applied to data and analytics products, including roadmap ownership and prioritization
• Excellent stakeholder management and communication skills, with the ability to align teams on shared metric definitions and outcomes
• Proven ability to translate ambiguous business needs into clear requirements for datasets and dashboards
• Working proficiency in SQL and strong fluency in data warehousing concepts, dimensional modeling, and event-based data
• Familiarity with semantic layers or metric layers and the concept of governed, reusable metric definitions
• Strong execution and program management skills, including sprint rituals, backlog hygiene, and delivery predictability
• Ability to create clear documentation and knowledge management systems in a wiki environment (e.g., Confluence)
• Strong intuition for data quality, data reliability, and trust-building through SLAs, validation, and monitoring practices
• Ability to drive adoption through enablement, training, office hours, and stakeholder feedback loops

Skills

• Strong product management skills applied to data and analytics products, including roadmap ownership and prioritization
• Excellent stakeholder management and communication skills, with the ability to align teams on shared metric definitions and outcomes
• Proven ability to translate ambiguous business needs into clear requirements for datasets and dashboards
• Working proficiency in SQL and strong fluency in data warehousing concepts, dimensional modeling, and event-based data
• Familiarity with semantic layers or metric layers and the concept of governed, reusable metric definitions
• Strong execution and program management skills, including sprint rituals, backlog hygiene, and delivery predictability
• Ability to create clear documentation and knowledge management systems in a wiki environment (e.g., Confluence)
• Strong intuition for data quality, data reliability, and trust-building through SLAs, validation, and monitoring practices
• Ability to drive adoption through enablement, training, office hours, and stakeholder feedback loops

Qualifications

• 5+ years of experience in data product management, analytics engineering, or analytics program leadership
• Bachelor's degree in a quantitative or technical field such as Computer Science, Engineering, Statistics, Economics, or a related discipline
• Master's degree in a relevant field (e.g., Analytics, Data Science, MBA, or similar) is a plus
• Equivalent practical experience in data product management, analytics engineering, or analytics program leadership considered in lieu of formal education