Data Product Manager

- 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


- 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


EEO:


"Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of - Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans."