Requirements A minimum of a Bachelor's Degree in Computer Science or Information Management or equivalent A minimum of five to eight years' experience in a Data Management environment Key Performance Measures Develop, construct, test, and maintain data pipeline architectures that are reliable, scalable, secure, and aligned to business needs. Design and maintain integration patterns, including APIs, data exchange formats, interoperability standards, and integration protocols. Build data pipelines that are robust, modular, scalable, reproducible, deployable, and version controlled across relevant data and business domains. Quality assure data pipeline implementations to ensure alignment with approved data engineering frameworks, standards, and best practices. Monitor and optimise domain-specific data pipelines to ensure reliable data availability, performance, and sustainability over time. Recommend practical and innovative ways to improve data quality, reliability, performance, automation, and operational efficiency. Support the technical implementation of key data management capabilities, including: Data quality management Metadata management Master and reference data management Data architecture and data modelling Data security and privacy Data lifecycle and retention management Data cataloguing and data lineage Analytics enablement Maintain clear and accurate documentation for data pipelines, integration processes, migration activities, technical designs, and interoperability requirements. Manage and analyse data growth patterns to support infrastructure planning, responsible data sharing, and compliance with data retention frameworks. Engage stakeholders across the organisation to promote effective data management and support appropriate, responsible, and value-driven data usage. Provide input into data governance forums, technical review sessions, migration planning sessions, and delivery reporting structures. Ensure that data engineering and migration activities support reliable decision-making, regulatory alignment, operational efficiency, and long-term data maturity.