Why This Role Exists
Owns a Labs & CMC data domain end-to-end, with clear accountability for the pipelines, models and datasets digital workers build and operate in it, and sound independent judgement on design choices.
Makes digital workers more effective in that domain by improving the frameworks, tools and evaluations they use, so manual intervention falls measurably over time.
Delivers quality-assured scientific data to AI systems through data contracts, governed context and quality frameworks that hold up to the strict auditability mandates of life sciences.
Key Responsibilities
Domain Level Data and Agentic Engineering
Design pipelines for complex Labs & CMC use cases and direct digital workers to build and operate them, with clear, defensible architectural trade-offs.
Own the quality of agent-generated work in your scientific domain: review, test and approve it within the remit our operating model sets, and escalate what falls outside it to senior engineering leadership.
Improve how digital workers operate in your domain by refining the frameworks, tools and evaluations they use, iterating on reliability signals so manual intervention falls measurably over time.
Context & Semantics
Own the context for your Labs & CMC domain in the governed context layer: definitions, contracts, lineage and governance documentation that humans and digital workers rely on.
Contribute reusable components to the broader Labs and CMC context layer, such as metadata enrichment, classification or lineage capture, that other scientific domains can adopt.
Data Quality & Governance
Build an end-to-end data quality framework for your domain, encoding standards directly into the context layer with lineage tracking and governance documentation that passes audit.
Apply privacy, IP, and consent controls in AI data pipelines, and manage sensitive R&D data appropriately across the data lifecycle.
AI Data Infrastructure & Lifecycle
Design and build feature stores, vector databases or retrieval infrastructure for production AI systems within Labs & CMC, with data contracts that give each AI consumer consistent, quality-assured inputs.
Manage training datasets, evaluation sets and data versioning for AI projects in production and contribute to evaluation dataset design with a clear view of how data decisions affect scientific model quality.
Platform & Infrastructure
Build cloud data infrastructure independently using infrastructure as code and contribute reusable components to the team's shared data platform to reduce setup time for new Labs and CMC projects.
Design observability into pipelines from the start, and resolve infrastructure issues with minimal disruption to scientific consumers. Own incident response for your domain, with clear post-incident analysis and follow-through.
Raise team technical quality through code review and documentation, working closely with senior engineers and domain scientists to translate ambiguous requirements into well-scoped designs.
Basic Qualifications
6+ years of experience building and operating production data pipelines and data models.
Deep SQL and Python, with strong hands-on expertise in modern warehouses or lakehouses and in orchestration and transformation frameworks, such as Databricks, BigQuery, Airflow, dbt or Spark.
Experience designing and operating at least one AI agent or LLM-powered tool inside a data workflow, and fluent, daily use of AI coding assistants and agents with sound judgement on what to accept, change or reject.
Experience designing and building feature stores, vector databases or retrieval infrastructure for production AI systems, including data contracts for at least one AI consumer.
Experience managing training datasets, evaluation sets and data versioning for at least one AI project in production.
A track record of building data quality frameworks and lineage for a domain, and of applying privacy, consent and compliance controls in AI data pipelines.
Experience building cloud data infrastructure independently with infrastructure as code, with observability designed in from the start.
Preferred Experience
Led the design and delivery of a component of a larger data platform.
Experience with agentic systems in production: multi-step tool use, evaluations, guardrails and observability applied to operational data problems.
Experience with quality frameworks and with catalogue, metadata or lineage tooling.
Experience in regulated or high-stakes data environments, such as life sciences, labs, CMC, or healthcare, where strict data integrity is a mandate
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 Governance, Data Ingestion, Data Integration, ETL Development, 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 IN.recruitment-adjustments@gsk.com to discuss your needs.
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