GCP Data Engineer – Cloud‑Native Data Pipelines (Hybrid)

About the role

Join a global healthcare and AI research organisation that is accelerating scientific discovery through data engineering and machine learning. As a GCP Data Engineer you will build and operate cloud‑native data infrastructure that powers PyTorch‑based ML pipelines.

Key responsibilities

• Design, develop and maintain scalable data pipelines on Google Cloud Platform using Python.
• Manage large‑scale object storage for unstructured data within BigQuery.
• Build and optimise data integrations between BigQuery and relational SQL databases.
• Ensure efficient memory usage and performance when processing massive datasets.
• Implement monitoring, testing and documentation to guarantee production‑grade reliability.
• Participate in agile ceremonies, code reviews and technical design discussions.

Required profile

• Strong Python development experience.
• Hands‑on experience with cloud object storage for unstructured data in BigQuery.
• Practical PyTorch experience (dataset management, data‑loading pipelines, running workloads in the cloud) – one or two 6‑12 month projects is sufficient.
• 5+ years of cloud experience, preferably handling large numbers of files in cloud buckets.
• Familiarity with additional GCP services such as Cloud Run, Cloud SQL and Cloud Scheduler is a plus.
• Interest or experience in pharma/life‑sciences data is desirable.

Required skills

• Python
• Google Cloud Platform (BigQuery, Cloud Storage)
• PyTorch
• SQL

What we offer

• 6‑month contract at £500‑£550 per day (inside IR35).
• Hybrid working arrangement in London.