Staff, Data Engineer

Job Purpose : • 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 design the system those digital workers operate in.  • At the centre of that system is 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 Staff Data Engineer is accountable for implementing and evolving the governed context layer that humans and digital workers use across EAI, and is EAI's internal authority on data architecture for AI.  Responsibilities: • Implement and evolve the governed context layer as a product: one layer across enterprise knowledge, structured data, business vocabulary and modelled ontologies, queried by humans and digital workers at run time.  • Build the capabilities the layer depends on: reliable cataloguing across structured and unstructured data, AI-assisted metadata enrichment, context design for each use case, secure agent access over standard protocols such as MCP, and routing across sources.  • Make certified meaning machine-readable so that every number a digital worker returns resolves to one governed definition, with clear ownership and lineage.  • Define the architecture standards and shared frameworks that digital workers use to generate, test, deploy and monitor pipelines, reducing the integration cost of each new AI initiative.  • Set the operating model for agent-generated engineering work: what digital workers do autonomously, what requires human approval, how quality is evaluated and how incidents escalate. Lead the incidents digital workers cannot resolve, from detection to permanent resolution.  Required skills: • 10+ years in data engineering, including substantial time defining data architecture for production AI or ML systems at significant scale or complexity.  • Deep, hands-on experience building AI-grade data infrastructure: feature stores, vector databases, retrieval architectures, streaming data flows and evaluation datasets.  • Experience building context, metadata or semantic infrastructure that AI systems consume, such as catalogues, business glossaries, knowledge graphs or ontologies, exposed to agents over protocols such as MCP.  • Experience designing and operating AI agents or LLM-powered systems inside data workflows, including evaluation, guardrails and human-in-the-loop controls.  • A track record of defining data quality, lineage and governance standards for AI pipelines, including privacy, consent, PII handling and auditability, adopted across multiple data domains.  • Expert command of cloud data platforms and infrastructure as code, and a record of setting engineering standards that others adopted Education: • Bachelor's degree in Computer Science, Data Engineering or a related field, or equivalent practical experience.  Preferred skills: • Prior Staff, Principal or equivalent experience in an organisation operating a non-trivial data platform for AI.  • Experience in a regulated industry, such as life sciences, where data integrity and auditability are mandatory.  • Public technical contributions, such as talks, writing or open source, on data architecture for AI, context engineering or agent-operated data engineering.  #AGTech Skills Artificial Intelligence (AI), Data Analysis, Data Architecture Development, Database Engineering, Databricks Unified Data Analytics Platform, Data Engineering, Data Transformation, ETL Development, Machine Learning (ML), Python (Programming Language), Structured Query Langua