Job Description Collaboration Work within a cross-functional agile team to design, build, and deliver high-quality data solutions that support business and product outcomes. Partner with data analysts, data scientists, product, and engineering teams to translate business requirements into scalable technical solutions. Collaborate with platform, infrastructure, and AI teams to ensure the data platform supports the group’s AI-native strategy. Team Leadership Lead and mentor a team of around five data and analytics engineers , ensuring strong delivery standards, code quality, and best practice adoption. Drive a culture of ownership, accountability, and continuous improvement within the data engineering function. Provide hands-on guidance and technical leadership while maintaining high delivery velocity. Data Engineering Delivery Own end-to-end data pipeline development and maintenance using the modern data stack (Python, dbt, Airflow, Snowflake, Databricks, AWS). Lead development, testing, and deployment for cloud data warehouse and lakehouse environments. Design and implement logical and physical data models that ensure performance, scalability, and maintainability. Oversee Power BI semantic models , ensuring governance, consistency, and reusability across business reporting layers. Implement CI/CD automation and infrastructure-as-code practices (Terraform, Buildkite or equivalent). Platform Ownership & Optimisation Manage the data platform budget , including vendor management, cost optimisation, and resource utilisation. Drive platform reliability, scalability, and performance improvements. Ensure data platform security through robust access management, collaboration with Cyber and Platform teams, and secure-by-design practices. Oversee provisioning of AI and BI services across the platform, supporting the company’s AI transformation goals. Data Governance & Quality Ensure compliance with data governance, privacy, and security frameworks. Monitor and resolve data quality issues, ensuring completeness, accuracy, and integrity across data products. Champion documentation, lineage tracking, and transparency across all data assets. Issue Management & Documentation Maintain detailed documentation for data pipelines, models, and solutions. Support issue and incident management, driving root-cause analysis and preventive improvements. Contribute to internal design standards, training material, and onboarding resources.
