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The Data Product Manager is responsible for owning, shaping, and delivering data products that create measurable business value for the retail organisation. This role serves as the primary bridge between business stakeholders and data, analytics, and data science teams – translating commercial priorities, operational challenges, and strategic questions into well-defined data and analytical solutions. The Data Product Manager ensures that data initiatives are grounded in genuine business need, clearly scoped, and delivered in a way that drives adoption and tangible outcomes, while maintaining fast delivery, high standards of data quality, usability, and governance.
Minimum Qualifications
- Bachelor’s degree in Information Systems, Business, Analytics, Commerce, or a related field (or equivalent practical experience).
- Project Management certification or equivalent (Agile certification preferred).
Minimum Experience
- 5+ years’ experience in a data, analytics, business intelligence, or technology environment.
- Proven track record in a role that required translating business needs into data or analytical outputs – e.g. Business Analyst, Data Product Owner, Analytics Lead, or similar.
- Experience working directly with senior business stakeholders and managing competing priorities across multiple domains.
- Demonstrated experience delivering complex, cross-functional initiatives in a large organisation.
- Retail industry experience strongly advantageous.
Technical & Tooling Exposure (Advantageous)
- Familiarity with data warehousing, BI, and analytics platform concepts.
- Exposure to data science and advanced analytics solution types (e.g. segmentation, forecasting, recommendation).
- Exposure to cloud data platforms and modern analytics stacks.
- Understanding of data governance, privacy, and security principles.
- Familiarity with Agile delivery tools and ways of working.\
Competencies
- Data Product Management: Ability to define product vision, prioritise roadmaps and backlogs, and ensure data products deliver measurable business value.
- Business & Data Acumen: Strong understanding of business processes and the ability to translate complex business challenges into practical data and analytical solutions.
- Stakeholder Engagement & Influence: Builds trusted relationships with business and executive stakeholders, manages competing priorities and influences decisions across multiple domains.
- Requirements Analysis & Translation: Ability to facilitate discovery, clarify ambiguous requirements and translate business needs into clear briefs for data, analytics and technology teams.
- Strategic & Commercial Thinking: Connects data initiatives to strategic objectives, commercial priorities and measurable business outcomes.
- Delivery & Project Management: Strong ability to coordinate complex, cross-functional initiatives, manage scope, timelines, dependencies and risks, and drive delivery to completion.
- Prioritisation & Decision-Making: Makes pragmatic, value-driven decisions when balancing business demand, capacity, dependencies, risk and competing priorities.
- Data Governance & Quality: Understands and promotes data governance, privacy, security, data quality and consistent data definitions.
- Communication & Facilitation: Communicates complex concepts clearly to technical and non-technical audiences and facilitates productive decision-making and discovery sessions.
- Value Realisation & Continuous Improvement: Measures adoption, usage and business impact and uses stakeholder feedback and data to continuously improve data products and outcomes.
- Collaboration & Leadership: Works effectively across data engineering, analytics, data science, architecture and business teams, creating alignment around shared outcomes.
- Data Literacy & Digital Fluency: Understands modern data, BI, analytics and data science concepts sufficiently to shape solutions and engage credibly with technical specialists.
Key Responsibilities
1. Business Engagement & Requirements Translation
- Act as the primary point of engagement for business stakeholders across retail domains including Commercial, Store Ops, Supply Chain, Finance, Marketing, Smart Shopper, Clothing, Omnichannel and others.
- Proactively engage with business leaders and domain experts to surface data needs, untangle complex problems, and identify opportunities for analytical value.
- Facilitate structured discovery sessions and working sessions to define business questions, success criteria, and the shape of required solutions.
- Translate ambiguous business challenges into clear, well-defined briefs for data engineering, reporting, analytics, and data science teams – spanning dashboards, analytical models, data products, and data science outputs.
- Serve as the voice of the business within the data team, and the voice of data capability back to the business.
2. Data Product Ownership
- Own the vision, roadmap, and prioritised backlog for assigned data products.
- Facilitate prioritisation and capacity planning with Senior Managers and Exec in Data division
- Act as the single point of accountability for the value delivered by each data product.
- Ensure data products are fit for purpose, adopted by end users, and deliver measurable business outcomes.
- Continuously refine priorities based on business value, dependencies, and team capacity.
- Define and monitor success measures, KPIs, and adoption metrics for each product.
3. Stakeholder Management & Communication
- Manage stakeholder relationships across operational and executive levels, balancing competing priorities across multiple domains.
- Maintain clear, proactive communication on progress, risks, timelines, and trade-offs.
- Build trusted relationships that enable honest conversations about what data can and cannot deliver.
- Champion data literacy and the value of evidence-based decision-making across business teams.
4. Delivery Coordination
- Plan and manage delivery across multiple data initiatives, including scope, timelines, risks, and dependencies.
- Coordinate work across data engineers, analysts, data scientists, and external partners where applicable.
- Track delivery progress and ensure milestones and deadlines are met.
- Proactively identify and manage risks, issues, and scope changes.
- Apply pragmatic delivery processes appropriate to the initiative and business area, whether Agile or other project delivery approaches.
5. Data Governance & Quality
- Ensure data products comply with data governance, privacy, and security standards.
- Collaborate with data governance and architecture teams to align solutions with enterprise standards.
- Define and monitor data quality metrics relevant to each product.
- Champion consistent metric definitions, data definitions, and documentation.
6. Value Realisation & Continuous Improvement
- Track adoption, usage, and business impact post-delivery.
- Drive continuous improvement based on user feedback, stakeholder input, and performance data.
- Identify opportunities to enhance or extend existing data products over time.
Closing Date: 20 October 2026
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