Staff Data Engineer: Governed AI Context & Platform

GSK

Durham (CA)

Hybrid

USD 150,000 - 210,000

Full time

7 days ago

Be an early applicant

A complete application in a minute

Tailored resume and cover letter, ready to send.

Get past ATS filters

Job summary

GSK is seeking a Staff Data Engineer for Enterprise AI (EAI) based in the United States. You will implement and evolve the governed context layer that humans and digital workers rely on, building data infrastructure for AI, feature stores, vector databases, retrieval architectures and evaluation datasets.

You will define standards, governance and MLOps practices to enable scalable, auditable AI pipelines.

Qualifications

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).
Experience designing and operating AI agents or large language model (LLM)-powered systems within data workflows, including evaluation, guardrails and human-in-the-loop controls.
Experience defining data quality, lineage and governance standards for AI pipelines across multiple data domains, including privacy, consent, personally identifiable information (PII) handling and auditability.
Experience with cloud data platforms and infrastructure as code, including experience setting engineering standards.

Responsibilities

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.
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 escape. Lead the incidents digital workers cannot resolve, from detection to permanent resolution.
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.
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.
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.
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.