Data engineer for digital product profiles

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.

• 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.

• 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.
• Будет плюсом: 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

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