Data Products & Stewardship Manager, Corporate, SEAA

Why This Mission: 

The Data Products & Stewardship Manager partners with Corporate Function leaders and data delivery teams, working closely with Strategy & Insight, Finance, HR, Marketing/ PR and Supply Chain to proactively identify and solve business problems through analytics, data science and AI. The role translates business challenges into high-value use cases that accelerate intelligence, activation and value creation in support of SEAA’s ambition. By combining internal data with external intelligence and applying developing technical expertise across analytics, data science and AI, the role builds a more comprehensive understanding of business opportunities and guides the development of relevant and scalable solutions. 

The role also drives data stewardship across Corporate Functions, helping to establish clear data definitions, consistent business logic and trusted sources of data while supporting the effective resolution of data quality issues. It provides coordination and expertise while ensuring that ownership and accountability for data accuracy remain with the business domains and Corporate Functions that produce the data. 

Impact You Can Create In The Role: 

Data Stewardship and Governance Facilitation: 

• Partner with Corporate Functions and relevant business domains to identify Critical Data Elements, establish appropriate definitions and standards, and resolve related data quality issues. 

• Drive the adoption of data stewardship practices across business teams by embedding clear frameworks, tools and ways of working into day-to-day operations, ensuring governance is applied consistently without creating unnecessary complexity 

Data Quality Management:  

• Partner with business stakeholders to define appropriate Data Quality Rules and reporting for Critical Data Elements. 

• Identify, record and analyse data quality issues and their root causes, partnering with accountable business teams to escalate and drive remediation where required. 

Data Product Lifecycle Management for Corporate Functions: 

• Partner with Corporate Function leaders to identify and frame high-value business problems that can be addressed through analytics, data science or AI, supporting growth, improving decision-making and accelerating activation towards SEAA’s ambition. 

• Translate business problems into well-defined analytics, data science and AI use cases, specifying the expected business outcome, target users, data requirements, analytical approach and measures of success. 

• Oversee the lifecycle of Corporate Function / Cross-divisional data products, including dashboards, reports, datasets, advanced analytics and AI-enabled products, from ideation and experimentation through delivery, adoption and continuous improvement. Ensure that solutions provide relevant, timely and actionable insights for business decision-makers. 

• Own and actively manage the product backlog, determining which data products and use cases should be prioritised, phased or declined based on business value, strategic alignment, feasibility, data readiness and resource capacity. Partner with Data Platform & Engineering and specialist teams, which own detailed technical design and implementation. 

• Lead the BI development team and coordinate delivery across data engineering, data science and AI specialist teams, ensuring timely and high-quality delivery of data solutions. 

• Ensure business requirements are clear, granular and ready for engineering and analytics teams to deliver with minimal rework 

• Drive adoption of data products, strategic KPIs and self-service analytics capabilities by guiding business users and promoting consistent use of a single source of truth. 

Applied Analytics, Data Science & AI: 

• Assess the suitability and feasibility of different analytical approaches, working with technical specialists to determine when descriptive analytics, predictive modelling, optimisation or AI-enabled solutions are appropriate. 

• Support the design, experimentation and validation of data science and AI use cases, constructively challenging proposals to ensure that business value, user needs, data readiness, responsible AI requirements and implementation feasibility are addressed. 

• Identify and integrate relevant internal data and external intelligence sources, including databases, market research and industry insights, to strengthen business analysis, opportunity identification and decision-making. 

Your Success Measures 

•    Value Creation through Advanced Analytics, Data Science and AI: 

Analytics, data science and AI solutions address clearly defined business problems and demonstrate measurable improvement in decision quality, operational effectiveness, client experience or business performance. 

•    Data Quality & Fit-for-Purpose: 

Corporate data assets are accurate, complete, consistent and fit for their intended use, with material data quality issues escalated and resolved in a timely manner. 

•    Adoption & Usage: 

 Increased usage of dashboards, datasets and data products by target business users. 

•    Prioritization Discipline:  

The product backlog is actively managed, with clear rationale for what is prioritized, phased or declined - ensuring team capacity is focused on highest-value outcomes.  

•    Compliance & Security:  

No critical incidents related to data governance, privacy or security breaches. 

You are Energized by: 

Contribute to SEAA’s long term ambition and transformation:  

Making an Impact: Solving material business problems and seeing data and AI solutions improve decisions, operations and performance. 

Bridging Business and Analytics: Translating business challenges into clear analytics, data science and AI opportunities, and making complex technical topics accessible to decision-makers. 

Building Trust in Data: Helping the organization align on definitions, improve data quality and use one trusted version of the truth. 

Shaping and Scaling Solutions: Taking ideas from problem framing and experimentation through delivery, adoption and continuous improvement. 

What You Will Bring:

Capability Requirements 

•    Business Acumen & Entrepreneurial Mindset: 

Able to proactively identify data opportunities and use cases that create business value, evaluate initiatives based on impact, strategic alignment, feasibility and resource constraints, and translate them into practical actions that support growth. 

•    Business Analysis & Requirements Translation:  

Strong ability to engage stakeholders, clarify ambiguous needs and translate business objectives into structured requirements for BI designers, data engineers and analytics teams. 

•    Data Product Ownership:  

Experience managing data and analytical products through the full lifecycle, including prioritisation, experimentation, delivery coordination, adoption, user feedback and continuous improvement. Skilled in coordinating work across business, analytics, data science and engineering teams while managing competing priorities. 

•    Data Stewardship & KPI Management:  

Strong understanding of data definitions, metric logic, business rules, data ownership and data quality management. Able to facilitate stewardship of key data assets without creating unnecessary governance burden. 

•    Data Quality & Corporate Data Understanding:  

Able to assess whether division and corporate data assets, such as HR and finance data, are complete, reliable and fit for the intended business use. 

•    Stakeholder Management & Communication:  

Able to bridge business and technical perspectives, communicate complex data topics in accessible language and manage expectations across senior and working-level stakeholders. 

•    Documentation & Data Literacy Enablement:  

Disciplined in maintaining documentation of data sources, definitions, ownership and key business logic. Comfortable enabling users to understand, interpret and adopt data products. 

•     Applied Analytics, Data Science & AI Expertise 

Strong analytical foundation, with demonstrated technical capability in data analytics and a clear commitment to deepening expertise in data science and AI. 

Able to understand and assess analytical methodologies, data requirements, model outputs, assumptions and solution limitations, and to work effectively with data scientists, AI engineers, data engineers and BI specialists. Hands-on experience with relevant tools and technologies, including Power BI, Power Query, DAX, Python, SQL, Databricks and Microsoft Fabric, is an advantage. 

•     Responsible AI & Analytical Judgement: 

Able to assess the appropriate use of analytics, data science and AI based on business value, data readiness, feasibility, interpretability and risk. Understands the importance of responsible AI, privacy, security and human oversight when designing and deploying AI-enabled solutions. 

At CHANEL, we are focused on creating an inclusive culture that nurtures personal growth, contributing to collective progress. We believe the uniqueness of each individual increases the diversity, complementarity and effectiveness of our teams. We strongly encourage your application, as we value the perspective, experience and potential you could bring to CHANEL.