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.
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Skills
Agile Methodology, Cross-Functional Teamwork, Data Analysis, Design Thinking, DevOps, Prioritization, Product Development Research, Product Roadmapping, Project Management, Return on Investment (ROI), Software Development, Strategy Deployment, User Experience (UX), User Interface (UI) Design
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