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 on sustainable and reusable data foundations.
Contribute to continuous improvement of data engineering standards, modelling practices and delivery ways of working.

Your Success Measures

. Pipeline Reliability:Stable, automated and well-monitored data ingestion pipelines support key division and function data products with limited manual intervention.

. Reusable Data Foundation:Curated datasets and semantic data models are reusable across dashboards, analytics and AI-enabled use cases.

. Reporting Accuracy & Usability:Dashboards, reports and analytical views are accurate, intuitive and fit-for-purpose, enabling business users to easily access, interpret and act on trusted data.

. Dashboard Performance & Stability:Dashboards and reports are optimized for performance, with reliable refreshes, reasonable loading times and minimal recurring technical issues.

. Data Quality & Trust:High levels of data accuracy, completeness, consistency and timeliness are maintained across key datasets and data products.

. Consistency of Business Logic:KPI definitions, business rules and transformation logic are consistently applied across reporting, analytics and downstream consumption.

. Delivery Enablement:BI, analytics and data science teams can access business-ready data with reduced rework and clearer lineage.

. Compliance & Security:No critical incidents related to data governance, privacy, access control or security breaches.

You are Energized by

Building the Data Backbone: Creating reliable pipelines and models that make trusted data available for business decisions and future AI-enabled products.
Powering Both BI and AI from One Foundation: Designing data structures that serve multiple consumption patterns - dashboards today, conversational AI tomorrow - without rebuilding from scratch.
Turning Data into Business-Ready Reporting: Designing dashboards and reports that make complex data easier for business users to understand, navigate and act on.
Making Data Reusable: Turning fragmented source data into structured, scalable and well-documented data assets that can be used across multiple teams and use cases.
Solving Technical Data Challenges: Investigating data issues, improving data quality and ensuring that reporting and analytics are built on solid foundations.
Bridging Engineering and Business Needs: Translating business requirements into technical data structures that are practical, sustainable and easy to consume.
Collaboration: Working cross-functionally with Data Product, Data Stewardship, Market Digital Solution, IT, BI, analytics and data science teams.

What You Will Bring- Capability Requirements

Data Engineering & Pipeline Development:Solid experience designing, building and maintaining data ingestion pipelines, ETL / ELT workflows and transformation processes in modern data environments.
Data Modelling & Semantic Layer Design:Strong understanding of data modelling principles, dimensional modelling, data marts, semantic layers, KPI logic and reusable data structures.
SQL & Modern Data Platform Skills:Strong hands-on experience working with relational databases, cloud data platforms, data warehouses or lakehouse environments. Experience with Microsoft Fabric and/or Databricks is preferred.
Power BI & Dashboard Development:Hands-on experience building, enhancing and maintaining dashboards and reports in Power BI. Able to design clear report layouts, user-friendly navigation, filters, drill-downs and business-facing analytical views.
Dashboard Performance & Optimization:Able to optimize dashboard performance, including data model structure, query logic, refresh design, visual loading time and report usability.
Data Quality & Governance Mindset:Able to implement data quality controls, reconciliation checks, documentation and lineage practices that improve trust and reduce operational risk.
Requirements Translation & Technical Feasibility:Able to translate business and data product requirements into clear technical specifications, while assessing feasibility, dependencies and delivery effort.
AI-Ready Data Thinking (Good to have):Awareness of how data structures, semantic models and curated datasets are consumed by AI solutions (e.g., conversational AI, RAG). Willingness to learn and adapt data practices to support AI-enabled products.
Collaboration & Communication:Comfortable working across business stakeholders, Data Product, Data Stewardship, IT, BI, analytics and data science teams, with the ability to explain technical topics clearly.
Learning Agility in Data & AI:Maintains strong learning agility in data and AI, continuously upskilling to support evolving analytics and AI-enabled data product needs.

Data Platform and Engineering Manager, SEAA