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 DevOps, 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 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 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.  

As a Senior Data Engineer, you are a leading technical contributor who turns ambiguous scientific or technical challenges into well-specified data solutions. You bring deep expertise in distributed systems, data processing, cloud platforms, and modern software engineering. You champion best practices, lead technical design, mentor engineers and drive high-impact work across the data ecosystem. You ensure robustness of our services and serve as an escalation point in the operation of existing services, pipelines, and workflows. You should be deeply familiar with the tools of modern data engineering (e.g. Spark, Kafka, Storm, …) and of our customers and engaged with the open-source community surrounding them – potentially, even to the level of contributing pull requests.

You operate with a strong engineering mindset, prioritizing automation, reliability, metrics and well-instrumented pipelines. You also support emerging capabilities such as GenAI powered data services, LLM-enabled agents, vectorized feature pipelines and RAG workflows.

Key responsibilities include:

• Designs, builds, and operates data tools, services, workflows, etc that deliver high value through the solution to key business problems by leveraging modern data engineering tools (e.g. Spark, Kafka, Storm, …) and orchestration tools (e.g. Google Workflow, AirFlow Composer)

• Confidently optimizes design and execution of complex solutions in data ingestion and data transformation

• Enables data products optimized for AI/ML and GenAI workloads—high throughput, observable, feature-ready and governed

• Produces well-engineered software, including appropriate automated test suites, technical documentation, and operational strategy

• Implements modular, reusable components and microservices that accelerate development and reduce operational overhead

• Provides input into the roadmaps of upstream teams (e.g. Data Platforms, DataOps, DevOps) to help improve the overall program of work

• Ensure consistent application of platform abstractions to ensure quality and consistency with respect to logging and lineage

• Fully versed in coding best practices and ways of working, and participates in code reviews and partnering to improve the team’s standards

• Adhere to QMS framework and CI/CD best practices and helps to guide improvements to them that improve ways of working

• Provides technical leadership, code reviews, architectural guidance, and mentorship to junior engineers and serves as an escalation point for complex operational issues across pipeline and data services.