We are partnered with a global biopharmaceutical organisation dedicated to transforming the lives of patients and their families. With a strong commitment to delivering life-changing medicines for serious conditions — often where limited or no therapeutic options exist — this company combines a diverse portfolio of marketed therapies with a growing pipeline in oncology and neuroscience. Headquartered in Europe with operations, laboratories, and manufacturing facilities worldwide, they are driven by a patient-focused and science-led approach.

This role will offer you:

• A leading position in driving data engineering initiatives across multiple Research & Development areas, including Clinical, Pre-Clinical, Non-Clinical, Omics, Real World Data, and more.
• The opportunity to design and optimise advanced data pipelines, models, and repositories using cutting-edge AWS technologies.
• Cross-functional collaboration with international teams of scientists, researchers, and stakeholders.
• A high-impact role supporting innovation in both neuroscience and oncology.

Responsibilities:

• Lead the design, development, and maintenance of data pipelines for diverse R&D data sources.
• Create and optimise ETL/ELT processes for structured and unstructured data using Python, R, SQL, and AWS services.
• Build and maintain repositories and data warehousing solutions.
• Develop and maintain data quality frameworks, validation processes, and KPIs.
• Implement data versioning, lineage tracking, and regulatory compliance measures.
• Document data processes, architectures, and workflows in line with best DevOps practices.
• Collaborate with R&D researchers, data scientists, and stakeholders to deliver tailored solutions.
• Ensure compliance with global data privacy regulations such as GDPR and HIPAA.

You will bring:

• Expert proficiency in Python, R, and SQL for data processing.
• Advanced knowledge of AWS services, particularly S3, Redshift, FSx, Glue, and Lambda.
• Strong skills in relational database design and modelling, with experience in NoSQL and Graph databases.
• Experience with containerisation (Docker, Kubernetes/EKS).
• Knowledge of healthcare data standards such as CDISC, HL7, FHIR, SNOMED CT, OMOP, and DICOM.
• Familiarity with big data technologies, MLOps, and model deployment.
• Bachelor’s degree in a relevant field (Master’s preferred) and 5–7 years’ experience in data engineering, including work with healthcare, research, or clinical data.