Data Product Owner

TP, a global leader in human+AI business services, is building data products that power its operations, its clients’ services and its AI ambitions. As Data Product Owner you will take end-to-end ownership of the data product assigned to you: why it exists, who uses it, what value it delivers, whether it is adopted and whether its data can be trusted. You will also be its project manager, leading the roll-out to each country and client from design to go-live, and working with the data modellers, developers and stewards of the global team, with Data Engineering and with the regional teams. It is a rare chance to build a data product from the ground up, at global scale and with direct visibility to TP’s leadership.

Success in this role requires a blend of product thinking and disciplined delivery. Your primary responsibilities will include:

Setting the product direction

• Product Vision: Define, with the business owner, what the data product is for, who uses it and the value it must deliver, including for AI use cases.
• Roadmap: Turn the vision into a roadmap of use cases and country or client roll-outs, starting with the most data-mature, and keep it current.
• Product Specification: Own the product’s specification, with its definitions, quality rules and service levels (SLAs), and manage every change to it with the business owner and the engineering teams.
• Business Translation: Translate business needs into clear requirements and a prioritized backlog, designed for reuse across analytical, operational and AI use cases.

Managing execution

• Delivery Planning: Run the delivery plan as project manager, managing milestones, dependencies, risks and the capacity committed by each team.
• Roll-out: Lead each country and client roll-out, from the assessment of their systems and data maturity to the agreed approach, local access and resources, and handover after go-live.
• Coordination & Reporting: Coordinate the data modellers, developers and stewards of the global team, the data engineers and the regional teams, and keep TP’s leadership informed on progress.

Driving adoption and trust

• Adoption: Drive adoption with the business owners, and lead user onboarding and change management.
• Value Tracking: Set the product’s success KPIs and track usage, local reports replaced and value delivered against the business case.
• Data Rights & Quality: Make sure every release uses data TP has the right to use and meets the agreed quality standards and SLAs, with Legal, Privacy and the data stewards, in line with GDPR and the EU AI Act.

The First-Year Objectives

• Publish the Product Charter: Agree the product’s vision, business case, KPIs and roadmap with the business owner and leadership.
• Prioritize the Backlog: Agree with the business owners a prioritized backlog of use cases for the first roll-outs.
• Deliver the First Roll-out: Within six months, take the first countries or clients to go-live on plan, with agreed definitions, standard reports reconciled with the existing local reports and formal sign-off.
• Scale Wave by Wave: Within twelve months, deliver the next roll-outs on the committed plan, and agree with engineering and the regional teams the capacity for the following waves.
• Onboard the First Users: Name the product’s users and set a baseline for adoption, satisfaction and the local reports replaced.
• Meet SLAs and Data Rights: Report monthly on the product’s SLAs and confirm the data rights for every source and client before go-live.

Domain Expertise

• Education: Background in Computer Science, Engineering, Data Analytics, Business or a related field; an advanced degree is valued; product (Scrum, SAFe Product Owner) or project management (PMP, PRINCE2) certifications are an advantage; DAMA CDMP is a plus.
• Professional Background: 6+ years in data, BI or digital product ownership or delivery, including 3+ years leading multi-stakeholder data or analytics projects end to end.
• Data Product Practice: Working knowledge of data products, data mesh and the medallion (bronze-silver-gold) architecture, service-level agreements (SLAs) and data management frameworks such as DAMA-DMBOK.
• Data Platform Literacy: SQL and enough familiarity with a lakehouse platform (Databricks) and BI tools (Power BI) to challenge delivery and estimates.
• Regulation: Working knowledge of GDPR and the EU AI Act, and experience where access to data is contractually constrained.
• AI Readiness: Understanding of what AI use cases need from a data product, such as provenance, consent, lineage and consistent definitions, and of how AI assistants and agents consume it.
• Proven Scale: Experience delivering data or analytics products across multiple countries or client accounts, ideally including products that feed AI use cases such as predictive models or AI assistants.
• Backlog & User Stories: Able to turn business needs into clear user stories, acceptance criteria and a prioritized backlog.
• Stakeholder Leadership: Able to influence business owners, Legal, InfoSec, engineering and regional teams without direct authority.
• Technical Fluency: Comfortable engaging with engineers, architects and data stewards in their own language.
• Languages: Fluent English; Spanish or Portuguese is an advantage.

Mindset & Behaviors

• Product mindset focused on long-term usability, scalability and value.
• Designs for reuse, building modular data assets that avoid duplication.
• Anticipates future needs and pushes the team towards solutions and progress.
• Process Excellence: Champions process excellence, continuously driving enhanced efficiency, effectiveness, and quality across the entire organization.
• AI Proficiency: Champions the integration of AI concepts and technologies across the organization to enhance organizational efficiency and innovation.
• Data Literacy: Promotes a culture of data-driven leadership throughout the organization and creates the necessary environment for people to leverage data for informed decision-making.
• Collaboration: Inspires collaboration by leveraging shared ideas and resources to drive the organization toward achieving common goals.
• Communication: Communicates complex and strategic information to diverse stakeholder groups using various channels.
• Emotional Intelligence: Shapes a culture of empathy and trust, creating an environment where positive relationships amplify the impact of actions.
• Open-Mindedness: Shapes an environment of open-mindedness that encourages diverse perspectives, ensuring all voices are heard and considered.
• Critical Thinking: Evaluates complex information and arguments leveraging data and insights, promoting a culture of critical thinking throughout the organization.
• Solution Orientation: Creates an environment in which people focus on finding practical and effective solutions and ensures that those are realized.
• Entrepreneurship: Cultivates an entrepreneurial mindset that emphasizes creativity, calculated risk-taking, proactive problem-solving, and opportunity identification.

• Competitive Compensation: A highly competitive salary and performance-based bonus structure aligned with your experience and market standards.
• Global Environment: The opportunity to work in a dynamic, international, and multicultural environment with cross-border teams and global projects.
• Career Growth: Clear pathways for professional development, internal mobility, and long-term career progression within a rapidly growing global company.
• Cutting-Edge Technology: Work with the latest tools, platforms, and AI-driven technologies in the industry.