Senior Data Engineer

Senior Data Engineer – GSK – South San Francisco / Cambridge, MA1

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 industrialized automated end‑to‑end data services and pipelines. We apply standardized data models and mapping to ensure data is accessible for end users through user tools via APIs, embed best practices, and ensure compliance with Quality Management practices and data governance. We also acquire and process internal and external, structured and unstructured data in line with product requirements.

As a Senior Data Engineer, you will be a leading technical contributor who turns ambiguous scientific or technical challenges into well‑specified data solutions. You will bring deep expertise in distributed systems, data processing, cloud platforms, and modern software engineering, champion best practices, lead technical design, mentor engineers, and drive high‑impact work across the data ecosystem. You will also support emerging capabilities such as GenAI‑powered data services, LLM‑enabled agents, vectorized feature pipelines, and RAG workflows.

• Design, build, and operate data tools, services, and workflows that deliver high value for key business problems using modern data engineering and orchestration tools (e.g., Spark, Kafka, Storm, Google Workflow, Air Flow Composer).
• Optimize design and execution of complex solutions in data ingestion and data transformation.
• Enable data products optimized for AI/ML and GenAI workloads—high throughput, observable, feature‑ready, and governed.
• Produce well‑engineered software, including automated test suites, technical documentation, and operational strategy.
• Implement modular, reusable components and microservices that accelerate development and reduce operational overhead.
• Provide input into the roadmaps of upstream teams (e.g., Data Platforms, Data Ops, Dev Ops) to improve the overall program of work.
• Ensure consistent application of platform abstractions to preserve quality and consistency in logging and lineage.
• Participate in code reviews and partner to improve the team’s standards.
• Adhere to the QMS framework and CI/CD best practices, guiding their improvement to enhance ways of working.
• Provide technical leadership, architectural guidance, and mentorship to junior engineers, and serve as an escalation point for complex operational issues across pipelines and data services.

We value individuals who bring passion, curiosity, and a commitment to collaboration. You’ll work in a culture that encourages innovation, fosters learning, and rewards impact.

• PhD + 2 years, master’s + 4 years, or bachelor’s degree with 6+ years of data engineering experience in industry.
• Software engineering experience.
• Experience overcoming high volume and high compute challenges.
• Familiarity with orchestration tooling.
• Cloud experience.
• Experience with automated testing and design.
• Experience in Dev Ops‑forward ways of working.

• Deep knowledge and use of at least one common programming language (Python, Scala, Java), including documentation, testing, and operations/observability tool chains.
• Expertise in modern software development tools (Git/Git Hub, Dev Ops tools, metrics/monitoring).
• Cloud experience (AWS, GCP, Azure, Kubernetes),…