Data Engineer and Team Lead

At GSK, we want to supercharge our data capability to better understand our patients and accelerate our ability to discover vaccines and medicines. 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 a next‑generation, metadata‑ and automation‑driven data experience for GSK’s scientists, engineers, and decision‑makers, increasing productivity and reducing time spent on “data mechanics”
• Providing best‑in‑class AI/ML and data analysis environments to accelerate our predictive capabilities and attract top‑tier talent
• Aggressively engineering our data at scale, as one unified asset, to unlock the value of our unique collection of data and predictions in real‑time

Data Engineering is responsible for the design, delivery, support, and maintenance of industrialized automated end‑to‑end data services and pipelines. They apply standardized data models and mapping to ensure data is accessible for end users in end‑to‑end user tools through the 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, structured and unstructured data in line with product requirements.

This role is 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. They support the head of Data Engineering in building a strong culture of accountability and ownership in their team, as well as instilling best‑in‑class 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.

• Lead a team of data engineers in delivering data and knowledge products that advance GSK R&D
• Architect of the data delivery and operational strategy for their team, who can deconstruct a complex and ambiguous data or knowledge request into a detailed strategy to make decision, anticipate future issues, and drive engineering efficiencies
• Partners closely with other data engineering leads to conceptualize the design of new data flows aimed at maximizing reuse and aligning with an event‑riven microservice enable architecture
• Partner with other Data Engineering leads to architect an engagement model and optimal ways of working with the product management teams, able to design innovative strategy beyond the current enterprise way of working to create a better environment for the end users, and 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 organization

• Bachelor’s degree in Data Engineering, Computer Science, or Software Engineering
• 7+ years of professional experience
• Software engineering experience
• Cloud experience
• Experience in automated testing and design

• Masters or PhD
• Strong data engineering experience in industry
• Demonstratable experience overcoming high volume, high compute challenges
• Familiarity with orchestrating tooling
• 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
• Cloud experience: e.g., AWS,…