Data Engineer

About the job

Data Engineer

Global Supply Chain Digital & Technology

At GSK, we unite science, technology and talent to get ahead of disease together. We are a global biopharma company with a special purpose – to unite science, technology and talent to get ahead of disease together. We’re proud to be leading a healthcare revolution, and we’re looking for people who can help us deliver on our ambition.

Role Overview

The 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 through CMC 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 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.

In this role you will

• Set up and maintain CI/CD pipelines and integration flows that move digital product profile and related data between PDLM 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 in your day-to-day work to improve your own productivity and your team’s ways of working, sharing what works and applying sound judgement, including 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) 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.
• 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.
• Design and support integration patterns between PDLM and key downstream systems (such as MES, LES/LIMS, ERP, 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, 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 help turn lessons learned into reusable patterns, templates and knowledge.
• Define and maintain data and integration engineering standards, review engineering practice, and establish reusable models and patterns
• 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.

Why you?

Basic Qualifications:

We are looking for professionals with these required skills to achieve our goals:

• Bachelor's or Master's degree in Computer Science, Information Technology, Software Engineering or a related discipline, or equivalent practical experience.
• Significant experience in data engineering, integration engineering, DevOps or similar roles in complex enterprise or manufacturing environments.
• Experience designing and delivering data and integration solutions that span R&D, CMC and/or manufacturing domains, including PDLM or similar digital product profile platforms.
• Experience working with ontologies, controlled vocabularies or knowledge-graph technologies to link data across systems, in partnership with central ontology and data standards teams.
• Experience enabling AI/ML or advanced analytics through well-designed data sets, features and integration points.
• Practical use of AI-assisted development, agent-based workflows and prompt templates for engineering, testing, documentation and support, applied within Responsible AI, data privacy, cyber security and GxP controls.
• Experience integrating or working with manufacturing, lab or quality systems (e.g. MES, LES/LIMS, ERP, control or regulatory/CMC platforms).
• Experience working with DevOps or platform teams to deploy and operate data and integration services, including pipelines, environments, monitoring and incident response.

Preferred Skills:

If you have the following characteristics, it would be a plus:

• Relevant cloud, DevOps, security, source-control or agile certifications are desirable.
• Strong analytical and troubleshooting skills.
• Ability to explain data and ontology concepts in simple terms to non-technical stakeholders
• Clear technical writing and documentation discipline.
• Understanding of secure configuration, least privilege, secrets and audit traceability.
• Ability to collaborate across global product, engineering, quality, cyber and service teams.
• Continuous-improvement mindset focused on automation, reuse and reliable delivery.
• Proficiency in at least one programming language commonly used for data and integration work (e.g. Python, Java) and practical SQL skills for working with structured data.

Closing Date for Applications: Monday 16th November 2026 (COB)

Please take a copy of the Job Description, as this will not be available post closure of the advert.
When applying for this role, please use the ‘cover letter’ of the online application or your CV to describe how you meet the competencies for this role, as outlined in the job requirements above. The information that you have provided in your cover letter and CV will be used to assess your application.