Staff Engineering & Team Lead

Job description

Site Name:
London The Stanley Building

Posted Date:
Jan 6 2026

Business Introduction

The Onyx Research Data Platform organization represents a major investment by GSK R&D and Digital & Tech, designed to deliver a step change in our ability to leverage data, knowledge, and prediction to find new medicines. We are a full-stack shop consisting of product and portfolio leadership, data engineering, infrastructure and Dev Ops, data / metadata / knowledge platforms, and AI/ML and analysis platforms, all geared toward:

• Building an unified, automated, next-generation data experience for GSK's scientists, engineers, and decision-makers, increasing productivity, and reducing data friction
• Providing best-in-class AI/ML, GenAI and data analysis environments to accelerate our predictive capabilities and attract top-tier talent
• Aggressively engineering our data at scale to unlock the value of our combined data assets and predictions in real-time

Data Engineering is responsible for the design, delivery, support, and maintenance of industrialised automated end to end data services and pipelines. They apply standardised data models and mapping to ensure data is accessible for end users in end-to-end user tools through use of APIs. They define and embed best practices and ensure compliance with Quality Management practices and alignment to automated data governance.

They also acquire and process internal and external, structure and unstructured data in line with Product requirements.

Position Summary

As a Staff Data Engineer, you are responsible for building and leading a scrum team of world-class data engineers focused on building automated, scalable, and sustainable pipelines to account for evolving scientific needs. You deeply influence the engineering direction of the Onyx platform, ensuring we build the best-in-class capabilities across data ingestion, streaming, transformation, knowledge graphs, metadata, vectorized data pipelines and AI/GenAI integration.

You support the head of Data Engineering in building a strong culture of accountability and ownership in the team, as well as instilling robust engineering practices (e.g., testing, code reviews, Dev Ops-forward ways of working). They work in close partnership with our Platforms teams to ensure we have the right tools and ways of working, and with our Bioinformatics teams to ensure the use of appropriate schemas, vocabularies, and ontologies.

This role is for an engineer who combines hands-on expertise with strategic vision, operating both as a senior builder and a technical thought leader.

• Lead and mentor a team of data engineers in delivering data and knowledge products that advance GSK R&D
• Architect end-to-end data pipeline patterns, abstractions, and reusable frameworks that improve reliability, observability and developer experience and define standards for data modelling, lineage, metadata, vectorization and governance
• Partner closely with other data engineering leads to conceptualise the design of new data flows aimed at maximising reuse and aligning with an event-driven microservice enable architecture
• Collaborate with ML and GenAI teams to optimize data flows for fine-tuning, prompt engineering, and retrieval-augmented generation (RAG) and champion data best practices for GenAI workloads
• Design an innovative strategy beyond the current enterprise way of working to create a better environment for the end users, and be able to construct a coordinated, stepwise plan to bring others along with the change curve
• Standard bearer for proper ways of working and engineering discipline, including the QMS framework and CI/CD best practices and proactively spearhead improvement within their engineering area
• Exemplar leaders in their field of technical knowledge, keen on bettering their understanding and acting as the knowledge holder for the organisation

Basic Qualification

• Bachelors' degree, Data Engineering, Computer Science, Software Engineering, or related discipline
• Strong data engineering experience in industry
• Experience with agile software development environments using tools like Jira and Confluence
• Demonstratable experience overcoming high volume, high compute challenges
• Familiarity with orchestrating tooling
• Cloud experience (e.g., AWS, Google Cloud, Azure, Kubernetes)
• Experience in automated testing and design
• Experience with Dev Ops-forward ways of working
• Deep knowledge and use of at least one common programming language: e.g., Python, Scala, Java
• Deep experience with common big data tools (e.g., Spark, Kafka, Storm, …)
• Application experience of CI/CD implementations using git and a common CI/CD stack (e.g., Jenkins, Circle
  
  CI, Git Lab, Azure Dev Ops)
• Experience with Infrastructure as a Code and automation tools (i.e., Terraform)
• Expertise in data modelling, database concepts and SQL

Preferred Qualification

• Masters or PhD in Data Engineering, Computer Science, Software Engineering, or related discipline
• Familiarity…