Senior, Data Engineer

GSK•Philadelphia, PA

2d•Hybrid

About The Position

Enterprise AI (EAI) builds the data foundations on which GSK's AI systems depend. Its operating model uses digital workers to perform hands-on data engineering, including generating, testing, deploying and monitoring pipelines and remediating failures. Engineers direct this work and own the quality of delivered systems. A governed context layer makes data, meaning, permissions and history retrievable, and sits above data platforms and beneath the experiences and agents built on them. The Senior Data Engineer leads the design of production data systems for a team and its domains. The role defines architectural, quality and governance standards for the team and its digital workers; leads AI-grade data infrastructure and lifecycle practices; improves data quality and operational reliability; and mentors junior and mid-level engineers. The role partners with forward deployed engineers, ML engineers and AI product teams on production data requirements and communicates trade-offs clearly. The role is based in the United States and is offered as a hybrid position, with flexibility to work remotely while collaborating on-site.

Requirements

Bachelor's degree in Computer Science, Data Engineering or a related field, or equivalent practical experience.
At least 8 years of data engineering experience, including significant experience 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; examples include Databricks, BigQuery, Airflow, dbt and Spark.
Experience leading design of AI-grade data infrastructure for production AI systems, including evaluation datasets, retrieval architectures and real-time data flows.
Experience leading AI system data lifecycle management, including versioning, drift monitoring and governance of AI artefacts.
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 in data engineering workflows, including tool design, evaluation, observability, human-in-the-loop controls and team-adopted AI-assisted development patterns.

Nice To Haves

Technical lead experience for one or more data platforms or significant domains, from initial build through scale.
Experience building or improving internal developer platforms or data engineering tools, such as self-service pipelines, agent runtimes, or lineage and quality services.
Experience with catalogue, metadata, semantic or lineage infrastructure consumed by AI systems.
Experience in regulated or high-stakes data environments, such as life sciences, finance or healthcare.

Responsibilities

Lead design of production pipelines serving multiple downstream consumers and set the architectural standards used by the team and digital workers.
Define patterns for agent-generated work, including what digital workers can build and remediate, required checks, and when human review is needed.
Design digital-worker integration with orchestration, observability and incident workflows, including tool design, evaluations, guardrails and human-in-the-loop controls.
Lead responses to significant data quality incidents, from detection through permanent resolution, and improve team-wide pipeline quality.
Set standards for capturing domain definitions, contracts, classification and lineage in the governed context layer; lead team contributions to the layer and inform its evolution across EAI.
Define and encode data quality and governance standards in the context layer, drive adoption of quality frameworks, and lead initiatives with measurable outcomes.
Lead design of AI-grade data infrastructure, including evaluation datasets, retrieval architectures and real-time data flows.
Lead AI data lifecycle management, including versioning, drift monitoring and governance of AI artefacts; work with ML engineers on evaluation datasets and production model reliability practices.
Lead cloud data infrastructure design, drive infrastructure-as-code adoption and improve deployment reliability measurably.
Build platform components used by multiple engineers and digital workers to reduce setup time for data projects.
Improve team practices for testing, data CI/CD, observability, cost management and on-call operations.
Set and promote documentation and code quality standards for both human engineers and digital workers.
Mentor junior and mid-level engineers through pairing, review and direct feedback.
Partner with forward deployed engineers, ML engineers and AI product teams on production data requirements, challenge weak requirements and communicate trade-offs.
In the first 12 months, lead delivery of production data systems with pipelines meeting clear SLAs; establish agent-generated work patterns that reduce manual effort; lead a measurable data quality or governance initiative encoded in the context layer; and mentor engineers toward greater capability and independence.