Engineer II, Data Engineer

About the Role: Enterprise AI (EAI) builds the data foundations that GSK's AI systems depend on. In our operating model, digital workers do the hands-on data engineering. They generate pipelines, test and deploy them, monitor them in production and remediate failures. Our engineers direct that work and own the quality of what ships. Humans and digital workers both rely on a governed context layer. It makes data, meaning, permissions and history retrievable at the moment they are needed, and sits above our data platforms and beneath every experience and agent built on them.   The Data Engineer II independently owns a data domain. The role designs pipelines for complex use cases, owns the quality of what digital workers deliver in that domain, and keeps its context accurate and audit-ready. It also contributes reusable components to the platform and raises technical quality across the team.   Why This Role Exists: • Owns a data domain end-to-end, with clear accountability for the pipelines, models and datasets digital workers build and operate in it, and sound independent judgement on design choices. • Makes digital workers more effective in that domain by improving the frameworks, tools and evaluations they use, so manual intervention falls measurably over time. • Delivers quality-assured data to AI systems through data contracts, governed context and quality frameworks that hold up to audit.   Key Responsibilities: Agent-Operated Data Engineering: • Design pipelines for complex use cases and direct digital workers to build and operate them, with clear, defensible architectural trade-offs. • Own the quality of agent-generated work in your domain: review, test and approve it within the remit EAI's operating model sets, and escalate what falls outside it. • Improve how digital workers operate in your domain by refining the frameworks, tools and evaluations they use, iterating on reliability signals so manual intervention falls measurably over time. Context & Semantics: • Own the context for your domain in the governed context layer: definitions, contracts, lineage and governance documentation that humans and digital workers rely on. • Contribute reusable components to the context layer, such as metadata enrichment, classification or lineage capture, that other domains can adopt. Data Quality & Governance: • Build an end-to-end data quality framework for your domain, with lineage tracking and governance documentation that passes audit. • Apply privacy and consent controls in AI data pipelines and manage PII appropriately across the data lifecycle. AI Data Infrastructure & Lifecycle: • Design and build feature stores, vector databases or retrieval infrastructure for production AI systems, with data contracts that give each AI consumer consistent, quality-assured inputs. • Manage training datasets, evaluation sets and data versioning for AI projects in production, and contribute to evaluation dataset design with a clear view of how data decisions affect model quality. Platform & Infrastructure: • Build cloud data infrastructure independently using infrastructure as code and contribute reusable components to the team's data platform. • Design observability into pipelines from the start and resolve infrastructure issues with minimal disruption to consumers. Own incident response for your domain, with clear post-incident analysis and follow-through. Collaboration & Standards: • Raise team technical quality through code review and documentation, with specific improvements to shared practices. • Work closely with forward deployed engineers (FDEs) and AI engineering teams, translating ambiguous requirements into well-scoped designs and delivering data assets on time and to agreed quality standards. • Contribute to desig