Principal Data Engineer

Job Purpose: The Principal Data Engineer provides technical leadership for how product and process information is structured, connected and reused across digital products and manufacturing systems, with a particular focus on the digital product profile. The role supports the transformation of product and process data from R&D (Research & Development) through CMC (Chemistry, Manufacturing & Controls) into commercial by shaping how that data is modelled, integrated and exposed for use in downstream systems and analytics. The job holder works with product teams, R&D partners and manufacturing technology teams to define practical strategies for connecting data and systems (including the use of ontologies, knowledge graphs, integrations and AI (Artificial Intelligence) where appropriate), enabling more seamless reuse of product and process knowledge across platforms. They also help define and operate the technical foundations (data pipelines, integrations, environments and deployments) needed to run these digital and AI-enabled capabilities safely and at scale, and to demonstrate that product and process data is being reused across R&D, CMC and commercial systems with improved data quality and time-to-access.  Key Responsibilities: Set up and maintain CI/CD (Continuous Integration / Continuous Deployment) pipelines and integration flows to move digital product profile data between PDLM (Product Data & Lifecycle Management) and other systems, using agreed branching strategies and automated checks. Ensure pipeline, integration and mapping code is kept under version control, automatically tested and peer-reviewed before release, so changes remain traceable, repeatable and supportable.  Use approved AI tools and collaborate with AI and analytics teams to provision data pipelines, integrations, environments and access patterns that enable safe AI proofs of concept and AI-assisted mapping of data models and ontologies, improving productivity and ways of working while validating outputs and following GSK’s Responsible AI, privacy and security requirements. Own the design of data and integration services within enterprise architecture guardrails, documenting decisions, trade-offs and rationale in decision records and technical documentation.  Define environment promotion flows (e.g. DEV, TEST, VAL, PROD (Development / Test / Validation / Production) for data and integration services supporting R&D, CMC and commercial use of product and process data.  Support releases and integrations end-to-end: maintain pipelines, automate deployments, manage repositories and environments and ensure changes follow validation and change-control requirements.  Work with R&D and central ontology / knowledge-graph teams to implement and operate data and metadata structures (for example applying agreed ontologies, reference data and mappings) so digital product profile data can be linked and reused across platforms, without owning the ontology roadmap itself.