This is an opportunity to work on a high-impact data engineering project at the intersection of AI, machine learning, and large-scale data platforms. You will help build and enhance critical data products that support advanced analytics and scientific innovation, using modern cloud technologies and engineering best practices.
The Company
They are a global organisation investing heavily in data, AI, and machine learning to drive innovation across complex scientific and business challenges. Their teams combine strong engineering practices with cutting-edge technology to deliver scalable, data-driven solutions. You will join a collaborative environment where data engineering plays a central role in enabling future-facing AI initiatives.
The Role and Deliverables
Build, operate, and automate scalable data pipelines handling large and complex datasets.
Design data storage, transfer, and transformation processes within a cloud-based environment.
Develop automated ingestion, validation, harmonisation, and quality control workflows.
Deliver analysis-ready datasets for machine learning, analytics, and wider technical teams.
Write production-grade Python code that is tested, documented, and maintainable.
Collaborate with software engineers, machine learning engineers, and domain specialists to solve complex data challenges.
Your Skills & Experience
Strong experience using Python for data engineering and data transformation.
Strong SQL skills, including data modelling and schema design.
Experience building and maintaining ETL or ELT pipelines using orchestration frameworks.
Experience working with large-scale datasets stored in cloud platforms such as GCP, AWS, or Azure.
Experience with CI/CD, DevOps practices, and modern software development methodologies.
Ability to work independently, manage ambiguity, and make sound technical decisions.
Experience with BigQuery, Google Cloud, medical imaging data, DICOM, or AI-enabled development workflows would be advantageous.
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