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.
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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
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Please contact our Recruitment Team at IN.recruitment-adjustments@gsk.com to discuss your needs.
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