Data Products Manager
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Job Description
Data Products Manager leading AWS-based data platform for financial reporting and analytics at PayFacto. Ensuring trusted data for decision-making while managing a high-performing team.
Responsibilities:
- Define and evolve the product vision, strategy and execution plan for the enterprise data platform (data lake), data warehousing, shared data services, and data foundations for AI
- Translate business and analytics requirements into platform capabilities (data sharing, governance, integration, self‑service) and prioritize the product backlog accordingly
- Collaborate closely with internal teams to design and deliver scalable features (transformations, cataloging, access controls, APIs) using modern technologies
- Drive platform adoption and internal customer satisfaction by collecting feedback, continuously improving offered capabilities, and measuring value and impact
- Coordinate cross‑functional launches, ensure operational readiness (documentation, training, support), and oversee the full product lifecycle from ideation to decommissioning
- Manage, coach, and develop a team of data product managers, analysts, and/or engineers, including hiring, performance management, and professional development
Requirements:
- Minimum of 5 years of experience in product management or in implementing data platforms
- Experience with AI platforms, analytics solutions, or data‑centric products (asset/preferred)
- Proven track record designing, deploying, or managing data platforms, data ecosystems, or tools intended for technical users
- Experience leading cross‑functional teams (engineering, data science, design) and delivering platform capabilities at scale
- Demonstrated ability to define and drive a product vision, strategy, and roadmap that link business needs to technical capabilities
- Excellent prioritization, backlog management, and execution skills in agile environments, including coordination across multiple teams
- Strong systems thinking, communication, and influencing skills with stakeholders across varying technical levels
- Collaborative and empowering leadership style focused on team success, clarity, and progress
- Ability to understand AI supporting infrastructure (model deployment, feature stores, monitoring, retraining) and align it with business priorities
- Strong data literacy: understanding issues related to data collection, cleaning, labeling, storage, and governance, and their impact on model performance and risks (drift, bias, privacy)
Benefits:
- Medical and dental coverage from day one
- 24/7 telemedicine
- Group registered retirement savings plan (RRSP)
- Vacation and personal days
- The challenge of working at a fast‑growing, fast‑paced company
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