Data Product Manager
J
JobgetherData analytics
CanadaFull-TimeManager
SalaryCanadian base salary range of CA$86,000–CA$130,000
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Job Details
- Experience
- 5+ years of experience in Product Management, Data Product Management, Business Systems, Analytics, or a related discipline.
- Required Skills
- Artificial IntelligenceBusiness IntelligenceMachine LearningProduct ManagementDatabricksData analytics
Requirements
- Have 5+ years of experience in Product Management, Data Product Management, Business Systems, Analytics, or a related discipline.
- Demonstrate experience partnering with senior business stakeholders to set priorities, define requirements, navigate tradeoffs, and establish success measures.
- Have experience supporting AI, machine learning, copilots, agentic workflows, intelligent automation, or related technologies.
- Have a strong understanding of enterprise data platforms, reporting, business intelligence, analytics, data governance, and core data concepts.
- Be able to translate business needs into product specifications, technical requirements, user stories, acceptance criteria, and measurable outcomes.
- Have experience in collaborative product and engineering environments emphasizing rapid prototyping, iterative discovery, and early technical engagement.
- Have strong communication, facilitation, stakeholder management, and relationship-building skills.
- Familiarity with AI-assisted product workflows, prompt-based experimentation, rapid prototyping, or AI-generated specifications and documentation is an asset.
- Experience with Databricks, Snowflake, Microsoft Fabric, or equivalent modern data platforms is preferred.
- Exposure to enterprise SaaS environments and operational business metrics is an advantage.
- Have a bachelor’s degree or equivalent professional experience.
Responsibilities
- Own and prioritize the data product roadmap with business leaders.
- Identify and prioritize opportunities across data, analytics, automation, and AI.
- Translate business objectives into reusable data products and capabilities.
- Define success measures that connect product delivery to business outcomes.
- Establish data domains, ownership models, business definitions, KPIs, metrics, data contracts, and documentation.
- Maintain the enterprise data dictionary and business glossary.
- Promote data quality, lineage, governance, stewardship, and appropriate handling of sensitive data.
- Use AI-assisted prototyping, rapid experimentation, and iterative validation to explore product opportunities.
- Translate business problems into product specifications, technical requirements, user stories, and acceptance criteria.
- Partner with data, AI, business systems, and engineering teams on solution design, validation, testing, and adoption.
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