Data Product Manager

CanadaRemoteFull-timemid

Product ManagementData Product Manager

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Quick Summary

Overview

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Product Manager based in Canada.

Technical Tools

Product ManagementData Product Manager

This role owns enterprise data products that enable reporting, analytics, operational intelligence, and AI-powered workflows across the organization.
You’ll translate complex business needs into trusted, reusable data products and capabilities with measurable business value.
Working at the intersection of product, data, AI, engineering, and business systems, you’ll shape priorities and guide delivery from discovery through adoption.
You’ll establish trusted data domains, definitions, metrics, governance practices, and reusable foundations for data-driven decision-making.
The role also embraces AI-assisted experimentation, rapid prototyping, and modern approaches to product development and engineering collaboration.
Success is measured by the business outcomes enabled through better data, faster insights, stronger governance, and AI-enabled workflows.
This is an opportunity to help shape an evolving data and AI product function within a highly collaborative, remote environment.

Own and prioritize the data product roadmap by partnering with business leaders to identify high-value opportunities across data, analytics, automation, and AI.
Translate business objectives into reusable data products and capabilities, prioritizing initiatives based on business impact, operational efficiency, customer outcomes, AI readiness, and strategic priorities.
Define clear success measures that connect data product delivery to measurable business outcomes.
Establish trusted data domains, including ownership models, business definitions, KPIs, metrics, data contracts, and enterprise data documentation.
Maintain the enterprise data dictionary and business glossary while promoting consistency across reporting, dashboards, operational systems, and AI-enabled workflows.
Champion data quality, lineage, governance, stewardship, and the appropriate handling of sensitive data.
Use AI-assisted prototyping, rapid experimentation, and iterative validation to explore and shape data and AI product opportunities.
Translate business problems and data opportunities into product specifications, technical requirements, user stories, acceptance criteria, and actionable guidance for engineering teams.
Partner with Data Engineers, Applied AI Engineers, Business Systems, and other technical teams on solution design, prototype reviews, validation, testing, instrumentation, and adoption planning.
Help establish an AI-augmented product development lifecycle in which specifications, test cases, implementation guidance, documentation, and other deliverables can be accelerated through responsible use of AI and validated through human expertise.
Develop strong cross-functional relationships across product, data, AI, business systems, and engineering disciplines to ensure technical solutions align with business objectives, governance requirements, feasibility, and measurable outcomes.

Requirements

~1 min read

5+ years of experience in Product Management, Data Product Management, Business Systems, Analytics, or a related discipline.
Demonstrated experience partnering with senior business stakeholders to establish priorities, define requirements, navigate tradeoffs, and establish success measures.
Experience supporting AI, machine learning, copilots, agentic workflows, intelligent automation, or related technologies.
Strong understanding of enterprise data platforms, reporting, business intelligence, analytics, data governance, and core data concepts.
Proven ability to translate business needs into product specifications, technical requirements, user stories, acceptance criteria, and measurable outcomes.
Experience working in collaborative product and engineering environments that emphasize rapid prototyping, iterative discovery, and early technical engagement.
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 modern data platforms such as Databricks, Snowflake, Microsoft Fabric, or equivalent technologies is preferred.
Exposure to enterprise SaaS environments and operational business metrics is an advantage.
Bachelor’s degree or equivalent professional experience.