Senior Data Engineer

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 direct that work and own the quality of what ships.

Humans and digital workers both rely on 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 Senior Data Engineer leads the design of production data systems for a team and its domains. The role defines the architectural, quality and governance standards the team and its digital workers apply, leads the team's AI-grade data infrastructure, and mentors junior and mid-level engineers.

Key Responsibilities:

Agent-Operated Data Engineering

• Lead the design of production pipelines that serve multiple downstream consumers, and define the architectural standard the team and its digital workers apply.
• Define the team's patterns for agent-generated work within EAI's operating model: what digital workers build and remediate on their own in the team's domains, which checks their work must pass, and when it needs human review.
• Design how digital workers integrate into the team's orchestration, observability and incident workflows, with clear tool design, evaluations, guardrails and human-in-the-loop controls.
• Lead the response to significant data quality incidents, from detection to permanent resolution, and drive improvements to team-wide pipeline quality.

Context & Semantics

• Set the context standards for the team's domains: how definitions, contracts, classification and lineage are captured in the governed context layer, so humans and digital workers can rely on them.
• Lead the team's contributions to the context layer, and feed the team's operational experience into how the layer evolves across EAI.

Data Quality & Governance

• Define data quality and governance standards for the team's domains, encode them in the context layer so humans and digital workers apply them by default, and drive adoption of quality frameworks others use.
• Lead data quality initiatives with measurable outcomes, such as reduced error rates, improved downstream AI performance or improved audit readiness.

AI Data Infrastructure & Lifecycle

• Lead the design of AI-grade data infrastructure, including evaluation datasets, retrieval architectures and real-time data flows, and define the team's standards for it.
• Lead data lifecycle management for AI systems: versioning, drift monitoring and governance of AI artefacts. Work with ML engineers on evaluation dataset design, and shape quality practices that improve model reliability in production.

Platform & Infrastructure

• Lead cloud data infrastructure design for the team's domains, driving adoption of infrastructure as code and measurable improvements in deployment reliability.
• Build platform components used by multiple engineers and digital workers, reducing setup time for new data projects.
• Raise the team's operational bar across testing, CI/CD for data, observability, cost management and on-call practice.

Standards & Mentorship

• Set the documentation and code quality standard for the team, written to be followed by humans and digital workers alike, and drive adoption of practices others use as the baseline.
• Mentor junior and mid-level engineers through pairing, review and direct feedback, with visible improvement in their capability and confidence.
• Partner with forward deployed engineers (FDEs), ML engineers and AI product teams on the data requirements of production systems, challenging weak requirements and communicating trade-offs clearly.

Requirements:

• Bachelor's degree in Computer Science, Data Engineering or a related field, or equivalent practical experience
• 8+ years of data engineering experience, including significant time leading the design and operation of complex production data systems.
• Expertise with modern warehouses or lakehouses, orchestration and transformation frameworks, and batch and streaming architectures, such as Databricks, BigQuery, Airflow, dbt or Spark.
• Experience leading the design of AI-grade data infrastructure for production AI systems, including evaluation datasets, retrieval architectures and real-time data flows.
• Experience leading data lifecycle management for AI systems, including versioning, drift monitoring and governance of AI artefacts.
• A track record of defining data quality and governance standards for a team or domain that others adopted, with measurable outcomes.
• Experience leading cloud data infrastructure design with infrastructure as code.
• Experience designing and operating AI agents or substantial LLM-powered tools inside data engineering workflows, including tool design, evaluation, observability and human-in-the-loop controls, and AI-assisted development patterns that a team adopted.