The posting
About the Role
GSK R&D and Digital & Tech's collective goal is to deliver GSK impact, including the acceleration of the discovery and development of medicines and vaccines to patients. The Labs and CMC Tech function is central to this mission, driving the reimagining and business transformation of our global laboratory and CMC processes. We are actively transitioning from managing raw scientific data to generating structural scientific knowledge—the foundation for how R&D will discover future medicines in a technology-enabled ecosystem.
The Labs CMC Data, Agentic and Knowledge Engineering (DAKE) group architects the data, agentic and knowledge foundations upon which critical R&D processes and scientific workflows depend. Crucially, our operating model relies on a hybrid workforce: AI agents function as "digital workers" to execute hands-on engineering tasks. They generate pipelines, test and deploy them, monitor them in production and remediate failures.
Reporting into the Director of Data and Agentic AI Engineering (G5), the Associate Director, Data & Agentic AI Engineering, leads the design of production data systems for a team and its domains within Labs & CMC. The role defines the architectural, quality, and governance standards the team and its digital workers apply, leads the team's AI-grade data infrastructure, and mentors junior and mid-level engineers.
Why This Role Exists
- Production System Leadership: Leads the design of production data systems for a Labs & CMC team and its domains, including pipelines that serve multiple downstream consumers and the AI-grade infrastructure beneath them incorporating existing standards and approaches.
- Defining the Hybrid Workflow: Defines how the team works with digital workers through the architectural standards, quality checks and review patterns their work must meet within our operating model.
- Capability Mentorship: Improves and implements our Raises the engineering standards bar with respect to for the team by setting the code, documentation and governance standards others use as the baseline, and advances by mentoring of junior and mid-level engineers.
Key Responsibilities
Domain-Level Data and Agentic Engineering
- Design and build production data pipelines for your specific Labs and CMC domain, serving multiple downstream scientific consumers.
- Implement, and enforce and extend our your team's patterns for agent-generated work, defining exactly what digital workers build and remediate autonomously versus what requires human review within your domain.
- Integrate digital workers into your team's specific orchestration and observability workflows, applying clear guardrails, tool designs, and human-in-the-loop controls.
- Lead the response to significant data quality incidents within your team's purview, from initial detection through to permanent resolution.
Context & Semantics Layer
- Set the context standards for the team's Labs & CMC domains: how definitions, contracts, classification and lineage are captured in the governed context layer, so humans and digital workers can rely on them.
- Actively contribute to the broader Labs and CMC context layer by feeding your team's hands-on operational experience upward to evolve the platform.
Data Quality & Governance
- Execute data quality and governance standards for your team's domains, encoding them directly into the context layer so both humans and digital workers apply them by default.
- Lead local data quality initiatives to achieve measurable outcomes, such as reduced error rates, improved audit readiness, and better downstream AI performance.
AI Data Infrastructure & Lifecycle
- Extend our Design and deliver AI-grade data infrastructure for your team's specific AI system needss, including novel retrieval architectures, improved real-time data flows, and smart evaluation datasets.
- Manage the data lifecycle for AI systems within your domain, including versioning, drift monitoring, and the governance of AI artefacts.
Platform Engineering & Mentorship
- Design cloud data infrastructure for your team's domains, driving the adoption improve our of Infrastructure as Code (IaC) to improve and its deployment reliability.
- Build shared platform components used by multiple engineers and digital workers to reduce setup time for new Labs and CMC data projects.
- Set and enforce the code and documentation quality standards for your team, written explicitly for a hybrid workforce of humans and digital workers
- Mentor junior and mid-level engineers through pairing, code review, and direct feedback to visibly improve their capability and independence.
Basic Qualifications
- 8+ years of data engineering experience, including significant time leading the design and operation of complex production data systems.
- Deep expertise with modern warehouses or lakehouses, orchestration and transformation frameworks, and batch and streaming architectures.
- Demonstrated experience designing and operating AI agents or substantial LLM-powered tools inside data engineering workflows, including tool design, evaluation, observability and human-in-the-loop controls.
- Experience leading the design of AI-grade data infrastructure for production AI systems, including evaluation datasets, retrieval architectures and real-time data flows.
- Experience leading data lifecycle management for AI systems, including versioning, drift monitoring and governance of AI artefacts.
- A track record of defining data quality and governance standards for a team or domain that others adopted, with measurable outcomes.
- Experience leading cloud data infrastructure design with infrastructure as code.
Preferred Experience
- Technical lead for one or more data platforms or significant domains, from first build through to scale.
- Experience with catalogue, metadata, semantic or lineage infrastructure that AI systems consume.
- Experience working within highly regulated industries, such as life sciences, where strict data integrity and auditability are non-negotiable mandates.
Education
- Bachelor's degree in Computer Science, Data Engineering or a related field, or equivalent practical experience.
#AGTech
#GSK-LI
Skills
Computer Programming, Data Analysis, Data Architecture Development, Databricks Unified Data Analytics Platform, Data Engineering, Data Governance, Data Ingestion, Data Integration, Data Migrations, Data Modeling, Data Transformation, ETL Development, Performance Optimizations, Performance Tuning, Structured Query Language (SQL)
Why GSK?
Uniting science, technology and talent to get ahead of disease together.
GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.
People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.
Inclusion at GSK:
As an employer committed to Inclusion, we encourage you to reach out if you need any adjustments during the recruitment process.
Please contact our Recruitment Team at [email protected] to discuss your needs.
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