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
Partner with Data Science and Analytics to ensure datasets are analysis-ready and reusable across use cases
Create and maintain KPI frameworks and “single source of truth” metric definitions to reduce metric disputes
Own dashboard strategy: what to build, what to retire, and how to improve usability and adoption
Implement and run 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
Establish monitoring and processes for data quality issues, incident triage, and continuous improvement
Enable self-serve analytics by improving discoverability, documentation, training, and stakeholder enablement
Act as the primary cross-functional point of accountability for analytics data products from concept through launch and iteration.

Required Skills & Qualifications

Strong product management skills applied to data and analytics products, including roadmap ownership and prioritization
Ability to think long-term about foundational data assets, not just short-term reporting outputs
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
Experience with BI/dashboarding ecosystems (e.g., Looker, Tableau, Power BI) and building scalable dashboard suites
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
Hands-on experience running Jira workflows and building operating mechanisms for intake, planning, and release management
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
Comfort partnering with engineering teams on technical tradeoffs, scope decisions, and delivery planning
Ability to drive adoption through enablement, training, office hours, and stakeholder feedback loops.

Education & Experience

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.