About the Role:
Enterprise AI (EAI) builds the data foundations that GSK's AI systems depend on. In our operating model, digital workers do the hands-on data engineering. They generate pipelines, test and deploy them, monitor them in production and remediate failures. Our engineers direct that work and own the quality of what ships.
Humans and digital workers both rely on a governed context layer. It makes data, meaning, permissions and history retrievable at the moment they are needed, and sits above our data platforms and beneath every experience and agent built on them.
The Data Engineer II independently owns a data domain. The role designs pipelines for complex use cases, owns the quality of what digital workers deliver in that domain, and keeps its context accurate and audit-ready. It also contributes reusable components to the platform and raises technical quality across the team.
Why This Role Exists:
- Owns a 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 data to AI systems through data contracts, governed context and quality frameworks that hold up to audit.
Key Responsibilities:
Agent-Operated Data Engineering:
- Design pipelines for complex 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 domain: review, test and approve it within the remit EAI's operating model sets, and escalate what falls outside it.
- 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 domain in the governed context layer: definitions, contracts, lineage and governance documentation that humans and digital workers rely on.
- Contribute reusable components to the context layer, such as metadata enrichment, classification or lineage capture, that other domains can adopt.
Data Quality & Governance:
- Build an end-to-end data quality framework for your domain, with lineage tracking and governance documentation that passes audit.
- Apply privacy and consent controls in AI data pipelines and manage PII appropriately across the data lifecycle.
AI Data Infrastructure & Lifecycle:
- Design and build feature stores, vector databases or retrieval infrastructure for production AI systems, 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 model quality.
Platform & Infrastructure:
- Build cloud data infrastructure independently using infrastructure as code and contribute reusable components to the team's data platform.
- Design observability into pipelines from the start and resolve infrastructure issues with minimal disruption to consumers. Own incident response for your domain, with clear post-incident analysis and follow-through.
Collaboration & Standards:
- Raise team technical quality through code review and documentation, with specific improvements to shared practices.
- Work closely with forward deployed engineers (FDEs) and AI engineering teams, translating ambiguous requirements into well-scoped designs and delivering data assets on time and to agreed quality standards.
- Contribute to design reviews with clear trade-off reasoning and communicate technical choices clearly to engineering and non-technical stakeholders.
Required Experience:
- 10+ 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 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 PII controls in AI data pipelines.
- Experience building cloud data infrastructure independently with infrastructure as code, with observability designed in from the start.
- 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.
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, finance or healthcare.
Education:
- Bachelor's degree in Computer Science, Data Engineering or a related field, or equivalent practical experience.
#AGTech
#GSK-LI
Skills
Agentic AI, Computer Programming, Data Analysis, Databricks Platform, Data Engineering, Data Governance, Data Ingestion, Data Integration, ETL Development, Performance Tuning, Python (Programming Language), SQL Query Development, 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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