Staff, Data Engineer

Overview

Implement and evolve the governed context layer for Enterprise AI at GSK, acting as the internal authority on data architecture for AI.

What you'll do

• Implement and evolve the governed context layer as a product, unifying 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.

What you'll need

• 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.
• Bachelor's degree in Computer Science, Data Engineering or a related field, or equivalent practical experience.

Nice to have

• 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.