Data Platform and Engineering Manager, SEAA

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The Data Platform and Engineering Manager is responsible for building and maintaining reliable data ingestion pipelines, curated datasets, semantic data models that enable trusted reporting, advanced analytics and future AI-enabled data products across divisions and functions. The role acts as the technical backbone between source systems, business data needs and data product delivery teams. It ensures that data is properly integrated, transformed, modelled and made available in a scalable, reusable and governed way, so that business users and AI solutions can access consistent, high-quality data to support intelligence, activation and decision-making towards the LEAP ambition. The role works in close partnership with Data Product, Stewardship, Analytics and Data Science teams, translating business priorities into robust, scalable and reusable data solutions. The role also brings hands-on reporting and BI capability, supporting the development, enhancement and maintenance of dashboards, reports and analytical views. It ensures reporting outputs are built on trusted data foundations, aligned with standard KPI definitions and designed to be practical, intuitive and fit-for-purpose for business users. Impact You Can Create In The Role: Data Ingestion & Pipeline Engineering: Design, build and maintain data pipelines to ingest data from division, function and enterprise systems into the data platform. Ensure data pipelines are scalable, reliable and monitored for timeliness, completeness and quality. Work with IT, global data teams and local stakeholders to understand source system structures, data availability and integration constraints. Support automation and industrialization of recurring data ingestion processes, reducing manual effort and operational risk. Curated Dataset & Semantic Model Development: Translate business data requirements into structured, reusable and well-documented data models. Build semantic layers that enable consistent definitions of KPIs, metrics, dimensions and business rules across dashboards, data products and AI-enabled solutions Develop curated datasets and data marts that make data easier to consume for BI, analytics and future AI-enabled use cases. Design data models with reusability, scalability and future productization in mind. Ensuring the same foundation can serve both reporting and AI consumption. Business and AI-Ready Data Foundition: Partner with Data Product and Stewardship teams to translate prioritized use cases into clear technical data requirements. Partner with the data science and AI team to understand downstream data requirements for AI use cases, ensuring data is structured, documented and accessible for model consumption. Ensure data models, curated datasets and data structures are designed to be consumable by AI solutions - including conversational AI, retrieval-augmented generation (RAG) and other AI-enabled products built by the AI team. Prepare clean, structured and business-ready datasets to support dashboards, reports, advanced analytics and future AI-enabled products. Work closely with data visualization, analytics and data science teams to ensure data models meet downstream consumption needs. Support root-cause analysis when data discrepancies, reporting issues or quality concerns arise. Dashboard & Business Intelligence Delivery: Design, build, enhance and maintain dashboards, reports and analytical views to support division and function decision-making. Translate business reporting needs into clear report logic, data structures, visualizations, filters and drill-down requirements. Partner with business stakeholders and Data Product & Stewardship teams to validate reporting outputs, clarify metric logic and ensure reports are accurate, usable and fit-for-purpose. Support report performance optimization, layout improvement, usability enhancement and recurring reporting automation. Ensure reports and dashboards are built on governed data models, consistent KPI definitions and trusted business logic. Data Quality, Governance & Documentation: Implement data quality checks, reconciliation logic and monitoring across ingestion, transformation and modelling layers. Maintain clear documentation on data lineage, transformation logic, metric definitions, model dependencies and known limitations. Ensure alignment with data governance principles, including access control, data ownership, naming conventions and standard definitions. Collaborate with Data Stewards and business owners to resolve data quality issues and improve trust in data products. Cross-Functional Delivery & Technical Partnership: Act as a technical partner to business-facing data roles, helping assess feasibility, effort and dependencies for new data product requirements. Coordinate with global and regional data platform teams to ensure local needs are reflected while maintaining alignment with enterprise architecture standards. Provide technical guidance to ensure data products are built

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