Staff Data Engineer

Entity: Technology Job Family Group: IT&S Group Job Description: Staff Data Engineer Individual Contributor, Grade G About the role The Staff Data Engineer plays a critical role in shaping, designing and delivering data and AI solutions that translate complex business needs into scalable, secure and reusable capabilities. Operating at staff level, this role provides senior technical leadership across data products, data platforms and AI-enabled solutions, with clear accountability for end-to-end system design, from high-level architecture through to low-level implementation design. This is a hands-on engineering leadership role. The postholder independently identifies problems to solve, leads the technical design of solutions and acts as technical lead across one large-scale or multiple medium-scale delivery workstreams. A strong command of modern engineering patterns and practices is essential, as this role helps set the engineering standard for others to follow. The role carries clear technical leadership accountability without line management responsibility. It guides delivery teams, influences internal and third-party engineering outcomes, and ensures solutions are well designed, maintainable, secure and fit for enterprise use. The postholder is also expected to mentor others and actively contribute to engineering excellence across the wider team. You will work with This role sits within the Finance Technology team and works closely with data engineers, data analysts, AI and software engineers, product managers, architects, cyber, platform and business stakeholder teams. It also partners with central technology and architecture teams to ensure solutions are designed for reuse, interoperability and future scalability, rather than being developed in isolation. You will deliver • End-to-end system design: Translate high-level architectural direction into detailed, buildable solution designs. This includes decomposing architectural patterns, such as event-driven ingestion, medallion lakehouse and RAG pipelines, into concrete engineering specifications including table schemas, transformation logic, interface contracts, retry and error-handling strategies, SLA thresholds and observability requirements. Produce HLD artefacts that communicate solution intent and platform choices, alongside LLD artefacts that delivery engineers can implement without ambiguity. Design outputs should be reviewable through architecture governance and usable as the baseline for technical assurance. • Strong engineering practices and patterns: Set and reinforce standards for software design, code quality, testing, CI/CD, observability and documentation. Advocate for and apply established engineering patterns, including event-driven architecture, microservices, pipeline orchestration, RAG and layered data architecture, using sound judgement on when each pattern is appropriate. Apply these patterns effectively across data-intensive pipelines, products and applications. •