Data Integration & Systems Senior Engineer

Responsibilities

• Data Strategy, Standards & Governance
• Coordinate and contribute to the development, documentation, and execution of the site end-to-end data strategy in alignment with network direction.
• Apply and reinforce data standards, enhancing deployment and adherence to ensure consistency, integrity, and compliance in how data is structured, stored, and used.
• Ensure data-related work is executed in line with applicable quality, validation, cybersecurity, and regulatory requirements, following established SOPs and governance processes.
• Contributes to and interprets global data standards for local implementation by providing site operational knowledge
• System Integration & Data Flow Enablement
• Design, execute and support core system integrations, ensuring optimised, secure, and compliant data flow and connectivity across system layers
• Monitor and support data connectivity, proactively identifying and resolving integration issues, data quality gaps, or performance risks within defined scope, escalating where appropriate.
• Execute and improve implementation, configuration, and lifecycle management of site historians and data platforms to enable operational reporting, analytics, and continuous improvement.
• Develop, enhance and rationalise local application lifecycle management, including development, configuration, upgrade, enhancements, rationalization, retirement and archiving as well as connectivity to data sources
• Maintain accurate asset, configuration, and data mapping documentation to support lifecycle management, validation, and inspection readiness.
• Participate in incident, deviation, and recovery activities to ensure timely restoration of services while maintaining data integrity and validated state control.
• Provide technical review and guidance on integration and solution design decisions within site scope.
• AI, Analytics & Innovation Enablement
• Partner with site operational, process teams and digital innovation teams to implement data-driven use cases, translating business problems into data requirements.
• Enable use of data for analytics, AI, and innovation initiatives by ensuring data availability, reliability, and alignment to defined standards.
• Support scaling and industrialization of AI and analytics use cases.