GCP Data Engineer

GCP Data Engineer£500 - £550 per day inside IR35 6-month contract Hybrid working in London We're working with a global healthcare and AI research organisation at the forefront of applying data engineering and machine learning to accelerate scientific discovery. Their work supports large-scale, domain-specific datasets that power research into life-changing treatments. They're now looking for a GCP Data Engineer to join a multidisciplinary team responsible for building and operating robust, cloud-native data infrastructure that supports ML workloads, particularly PyTorch-based pipelines. The RoleYou'll focus on designing, building, and maintaining scalable data pipelines and storage systems in BigQuery, supporting ML teams by enabling efficient data loading, dataset management, and cloud-based training workflows. Key Responsibilities • Design and build cloud-native data pipelines using Python on GCP • Manage large-scale object storage for unstructured data within BigQuery • Build and optimise data integrations with BigQuery and SQL databases • Ensure efficient memory usage and performance when handling large datasetsImplement monitoring, testing, and documentation to ensure production-grade reliability • Participate in agile ceremonies, code reviews, and technical design discussions Tech Stack & ExperienceMust Have • Strong Python development experience • Hands-on experience with cloud object storage for unstructured data within BigQuery • PyTorch experience, particularly: • Dataset management • Data loading pipelines • Running PyTorch workloads in cloud environments We are not looking for years of PyTorch experience - one or two substantial 6-12 month projects is ideal • 5+ years cloud experience, ideally working with large numbers of files in cloud buckets Nice to Have • Experience with additional GCP services, such as: • Cloud Run • Cloud SQL • Cloud Scheduler • Exposure to machine learning workflows (not ML engineering) • Some pharma or life sciences experience, or a genuine interest in working with domain-specific scientific data Please send your CV