Staff, Data Engineer

GSK; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

GSK’s Enterprise AI (EAI) seeks a Staff Data Engineer to own the governed context layer that powers AI systems across the organization. You will define architecture standards, build scalable data infrastructure, and ensure governance, privacy and lineage of data used by humans and digital workers.

Join a team shaping data foundations, partnering with ML engineers and platform teams to drive initiatives from concept to production.

Qualifications

• Bachelor's degree or equivalent in CS or related field.
• 10+ years of data engineering experience, including AI/ML data architectures.
• Experience building data infrastructure for AI systems (feature stores, vector DBs, pipelines).
• Experience building context/metadata/semantic infra used by AI systems (catalogues, glossaries, knowledge graphs).
• Familiarity with Model Context Protocol (MCP) is a plus.

Responsibilities

• Implement and evolve the governed context layer as a product across enterprise data.
• Define architecture standards and shared frameworks for agent-generated engineering.
• Shape AI data infrastructure strategy including feature engineering, vector/storage and data lifecycle.
• Define data governance standards for AI pipelines with privacy, auditability and bias controls.
• Own platform components, reliability, performance, cost, and security.

Skills

Data engineering

AI data infrastructure

Context/metadata/semantic infra

MCP (Model Context Protocol)

Data catalogues/glossaries/ontologies

Education

Bachelor's degree in Computer Science or related field

Job description

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.

Owns the governed context layer as the shared foundation every human and digital worker in EAI relies on, accountable for its architecture, implementation and evolution.

Defines the system digital workers engineer within through the standards, frameworks, contracts and guardrails that let them build and operate pipelines safely, consistently and at scale.

Raises the standard of data engineering for AI across EAI through the architecture, governance and engineering practices others adopt, and through the senior engineers the role develops.

Key Responsibilities:

• Context & Semantics
  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.
• Agent-Operated Data Engineering
  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 escalates. Lead the incidents digital workers cannot resolve, from detection to permanent resolution.
• AI Data Infrastructure & Lifecycle
  Shape EAI's AI data infrastructure strategy across feature engineering, vector and retrieval infrastructure, streaming data flows and evaluation data design, building shared components that multiple AI initiatives depend on in production.
  Define EAI's MLOps data practices: versioning, lineage, drift monitoring, evaluation dataset governance and model data lifecycle management, extended to the context and evaluation data that digital workers rely on.
• Governance & Responsible Data
  Define EAI's responsible data governance standards for AI pipelines, covering privacy, consent, PII, auditability and bias controls, and encode them in the governed context layer so humans and digital workers apply them by default.
  Shape data quality and lineage at scale across multiple data domains, with measurable outcomes in error rates, downstream AI performance and audit readiness.
• Platform & Infrastructure
  Design EAI's data platform architecture with infrastructure as code throughout and platform-as-product thinking, so new data and AI projects start quickly and consistently.
  Build shared platform components that AI initiatives, engineering teams and digital workers depend on in production, and own their reliability, performance, cost and security.
• Standards & Technical Leadership
  Define data engineering standards for code, documentation and review, written to be followed by humans and digital workers alike, and drive their adoption across EAI.
  Develop senior engineers towards greater architectural independence, and partner with forward deployed engineers (FDEs), ML engineers, platform engineers and domain experts on the data requirements of production AI systems.

Why You?

Basic Qualification

• Bachelor's degree in Computer Science, Data Engineering or a related field, or equivalent practical experience.
• 10+ years of data engineering experience, including experience defining data architecture for production AI or machine learning (ML) systems.
• Experience building data infrastructure for AI systems, including feature stores, vector databases, retrieval architectures, streaming data pipelines and evaluation datasets.
• Experience building context, metadata or semantic infrastructure used by AI systems, such as data catalogues, business glossaries, knowledge graphs or ontologies, exposed to AI agents through protocols such as Model Context Protocol (MCP).
• Exper