Principal Data Engineer

Job description

About the Team: Enterprise Systems is the backbone of GSK’s internal operations in Digital & Tech, enabling GSK Global Functions teams such as Finance, Procurement, HR, Legal, Compliance, WREF (Workplace & Real Estate Facilities), CGA (Communications & Government Affairs) and Corporate Secretariat to run seamlessly. This team designs and delivers cutting-edge digital platforms that power critical processes, ensuring efficiency, compliance, and scalability across the enterprise. Enterprise Systems Management & Business Operations delivers digital solutions that support Enterprise Systems running as a high-performing organisation. This team drives financial discipline, operational rigour and strategic alignment across the ES portfolio that safeguard GSK’s integrity and reputation globally. By joining this team, you’ll help shape systems that support thousands of employees worldwide, driving innovation and reliability in the tools that keep GSK moving forward. Job Purpose: The Lead Data & Transformation 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. Design and support integration patterns between PDLM and key downstream systems (such as MES (Manufacturing Execution System), LES (Laboratory Execution System)/LIMS (Laboratory Information Management System), ERP (Enterprise Resource Planning), control and regulatory/CMC platforms) so product and process data can flow reliably from source to execution and submissions. Make practical use of AI-assisted development, agent-based workflows and prompt templates for engineering, testing, documentation and support, validating every AI output for accuracy, security, (Good Practice) GxP/data integrity and maintainability before it is committed or released, and applying Responsible AI, data privacy and cyber security controls. Monitor and troubleshoot data pipelines, integrations, environments and related services, including performance and data quality, and drive sustainable remediation for recurring issues. Document data flows, integration contracts, deployment patterns and runbooks and keep this documentation aligned with evolving product and system changes, including decision records and standards. Coach engineers and analysts working on digital product profile, data and integration work, and define and maintain data and integration engineering standards by reviewing engineering practices and establishing reusable models, patterns, templates and knowledge. Drive continuous improvement in reliability, observability, performance and security for data and integration services supporting the digital product profile and transformation use cases, using measures such as data reuse, data quality and time-to-access. Basic Qualifications: Bachelor's or master's degree in computer science, Information Technology, Software Engineering or a related discipline, or equivalent practical experience. Relevant cloud, DevOps, security, source-control or agile certifications are desirable. Minimum 7 years’ experience in data engineering, integration engineering, DevOps or similar roles in complex enterprise or manufacturing environments. Experience in designing and delivering data and integration solutions spanning R&D, CMC and/or manufacturing domains, including PDLM or equivalent digital product profile platforms. Experience in working with ontologies, controlled vocabularies or knowledge-graph technologies to link data across systems, in collaboration with central ontology and data standards teams. Experience in enabling AI/ML or advanced analytics through well-designed datasets, features and integration points. Experience in using AI-assisted development, agent-based workflows and prompt templates for engineering, testing, documentation and support, with application of Responsible AI, data privacy, cyber security and GxP controls. Experience in integrating with or working on manufacturing, laboratory or quality systems (for example MES, LES/LIMS, ERP, control or regulatory/CMC platforms). Experience in partnering with DevOps or platform teams to deploy and operate data and integration services, including pipelines, environments, monitoring and incident response. Preferred Qualifications: Apply analytical and troubleshooting skills applied to complex data and integration issues. Ability to explain data and ontology concepts in clear, accessible language to non-technical stakeholders. Regularly produces and maintains technical documentation, including keeping records, integration designs, and standards accurate and up to date. Working knowledge of secure configuration, least-privilege access, secret management and audit traceability. Experience collaborating across global product, engineering, quality, cyber and service teams. Applies continuous-improvement practices focused on automation, reuse and reliable delivery. Proficiency in at least one programming language commonly used for data and integration work (for example, Python or Java) and practical SQL skills for working with structured data. #AGTech #GSK-LI Skills Agile Methodology, Cross-Functional Teamwork, Da