Manager - Data Products

The Data Products Manager will serve as the critical bridge between Omnichannel's business and analytics needs and the central Data team. They will translate business needs into actionable requirements, ensuring the seamless development and implementation of data-driven features, metrics, and systems for Omnichannel. Their ability to understand analytics requirements, work with complex data structures, and collaborate across Omnichannel and central Data will help scale and optimize the data ecosystem and enhance reporting capabilities, while ensuring what gets defined for Omnichannel is properly documented, governed, and delivered on time. Requirement Gathering & Translation Collaborate with the Omnichannel Data Analytics team to understand their needs for new metrics, features, and functionality. Translate analytical requirements and customer-related KPIs such as Customer Lifetime Value (CLV), Churn Rate, Conversion Rates, Average Order Value (AOV), and Customer Acquisition Cost (CAC) into clear requirements for the central Data team. Partner with business stakeholders to define key metrics related to customer behaviour, segmentation, and personalization strategies in retail. Ensure data systems and analytics tools support retail KPIs, including sales performance, inventory turnover, and omnichannel engagement metrics. Data Product Roadmap Own and maintain the Omni data product roadmap, ensuring priorities align to business strategy, customer outcomes and available Data capacity. Acceptance Criteria and Validation Define clear acceptance criteria with Analytics/business stakeholders and ensure delivered data products are validated against the original requirements before being considered complete. Privacy and Compliance Ensure data products and customer analytics requirements comply with relevant privacy, consent and data governance policies, particularly given this role deals heavily with customer data and personalisation. Catalog & Glossary Stewardship Act as the Data Steward reference point for Omnichannel customer KPIs: ensure every metric you help define is documented in the Data Catalog Glossary, with Definition, Formula / Calculation Logic, Domain, and Owner filled in not left to be done later. Work with the accountable Data Owner and Stewards to resolve conflicting definitions before a metric is built, so Omnichannel isn't shipping a KPI that later needs to be redefined. Link delivered dashboards, Explores, and reports to their corresponding Glossary entries as Related Assets, and apply or request Gold status where appropriate. Keep Glossary entries and asset links current as Omnichannel's reporting evolves retire or update links when a report is rebuilt or replaced. Prioritization Own first-line prioritization of Omnichannel's data and analytics requests, weighing competing stakeholder asks against Omnichannel's own goals. Submit prioritized requests into the central Data backlog all requests requiring build or engineering work are raised there, not routed around it. Work with central Data Product Managers and the Delivery & Prioritisation Lead as they review, re-rank, and sequence Omnichannel's requests alongside demand from other domains and technical feasibility. Cross-Functional Collaboration Act as the primary liaison between the Omnichannel Data Analytics team and the central Data team, ensuring effective communication and alignment. Represent Omnichannel's priorities in central backlog reviews and planning, and bring central Data's constraints and decisions back to Omnichannel stakeholders. Project Management Track the progress of Omnichannel's requests through the central backlog, ensuring timely delivery. Monitor progress, resolve roadblocks, and communicate updates to Omnichannel stakeholders. Documentation & Communication Document business requirements, user stories, and Glossary content clearly enough for central Data to build against without repeated clarification. Communicate complex concepts to both technical and non-technical stakeholders clearly and effectively. Core Knowledge & Experience Strong understanding of analytics concepts (e.g. metrics, KPIs, business logic) and how they translate into technical systems. Ability to work with the central Data team and translate business requirements into clear, buildable requirements. Familiarity with the Data Catalog, Glossary, and the Data Owner / Steward governance model or a fast willingness to learn it. Timeline-sensitive: able to follow up through a shared backlog and ensure delivery. Familiarity with data pipelines and databases (SQL and Python knowledge is a plus). Experience working with tools like Looker, Power BI, or similar visualization platforms. Exceptional organizational skills, with the ability to manage multiple priorities. Strong communication skills, with the ability to bridge the gap between technical and business teams. Proven experience in product management or a similar role, preferably in data analytics, retail analytics, or data infrastructure. Understanding of agile methodologies, such as sprints and user stories. Familiarity with customer-related retail KPIs, including Customer Retention, Basket Analysis, Revenue per Customer, and other metrics central to retail performance and loyalty programme.

Desired Candidate Profile

Strong understanding of analytics concepts (e.g. metrics, KPIs, business logic) and how they translate into technical systems. Ability to work with the central Data team and translate business requirements into clear, buildable requirements. Familiarity with the Data Catalog, Glossary, and the Data Owner / Steward governance model or a fast willingness to learn it. Timeline-sensitive: able to follow up through a shared backlog and ensure delivery. Familiarity with data pipelines and databases (SQL and Python knowledge is a plus). Experience working with tools like Looker, Power BI, or similar visualization platforms. Exceptional organizational skills, with the ability to manage multiple priorities. Strong communication skills, with the ability to bridge the gap between technical and business teams. Proven experience in product management or a similar role, preferably in data analytics, retail analytics, or data infrastructure. Understanding of agile methodologies, such as sprints and user stories. Familiarity with customer-related retail KPIs, including Customer Retention, Basket Analysis, Revenue per Customer, and other metrics central to retail performance and loyalty programme.