Staff, Data Engineer

GSK•Philadelphia, PA

22h•Hybrid

About The Position

The Staff Data Engineer is accountable for implementing and evolving the governed context layer used by humans and digital workers across Enterprise AI (EAI), and serves as EAI’s internal authority on data architecture for AI. The role owns the architecture, implementation and evolution of this shared foundation and defines the standards, frameworks, contracts and guardrails that enable digital workers to build and operate pipelines safely, consistently and at scale. The role establishes a governed context layer that unifies enterprise knowledge, structured data, business vocabulary and modelled ontologies, making them available at run time. It shapes AI data infrastructure, MLOps data practices, responsible data governance and EAI’s data platform architecture. It also sets engineering standards and develops senior engineers while partnering with forward deployed engineers, ML engineers, platform engineers and domain experts. This role is based in the United States and follows a hybrid working model, with on-site collaboration roughly two days per week. Remote work outside this arrangement is not available.

Requirements

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 systems.
Experience building AI data infrastructure, 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 LLM-powered systems in 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, PII handling and auditability.
Experience with cloud data platforms and infrastructure as code, including experience setting engineering standards.

Nice To Haves

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.
Experience defining pipeline architecture standards for enterprise AI and multiple AI initiatives.
Experience implementing and evolving governed context and semantic layers used by people and AI systems at run time.
Knowledge of data architecture for AI, including feature engineering, vector infrastructure, streaming and evaluation data design.
Experience with versioning, lineage, evaluation dataset governance and model data lifecycle management.
Experience designing cloud data platform architecture, applying infrastructure-as-code and platform-as-product practices, and building reusable platform components for multiple AI initiatives, engineering teams and AI systems.
Experience mentoring senior engineers, supporting architectural decision-making and advising on complex data challenges.

Responsibilities

Implement and evolve the governed context layer as a product spanning enterprise knowledge, structured data, business vocabulary and modelled ontologies, queried at run time by humans and digital workers.
Build reliable cataloguing across structured and unstructured data, AI-assisted metadata enrichment, use-case-specific context design, secure agent access over standard protocols such as MCP, and routing across sources.
Make certified meaning machine-readable, with governed definitions, ownership and lineage for data returned by digital workers.
Define architecture standards and shared frameworks for digital workers to generate, test, deploy and monitor pipelines, reducing the integration cost of new AI initiatives.
Set the operating model for agent-generated engineering work, including autonomy, human approvals, quality evaluation and incident escalation.
Lead incidents digital workers cannot resolve, from detection through permanent resolution.
Shape EAI’s AI data infrastructure strategy across feature engineering, vector and retrieval infrastructure, streaming data flows and evaluation data design; build shared production components for multiple AI initiatives.
Define EAI’s MLOps data practices for versioning, lineage, drift monitoring, evaluation dataset governance and model data lifecycle management, including context and evaluation data used by digital workers.
Define responsible data governance standards for AI pipelines covering privacy, consent, PII, auditability and bias controls, and encode controls in the governed context layer.
Shape data quality and lineage across multiple data domains, with measurable outcomes in error rates, downstream AI performance and audit readiness.
Design EAI’s data platform architecture using infrastructure as code and platform-as-product practices so new data and AI projects can start consistently and quickly.
Build and own shared production platform components, including their reliability, performance, cost and security.
Define data engineering standards for code, documentation and review for both humans and digital workers, and drive adoption across EAI.
Develop senior engineers toward greater architectural independence.
Partner with forward deployed engineers (FDEs), ML engineers, platform engineers and domain experts on data requirements for production AI systems.