Design, build, and govern cloud-based data platforms that turn heterogeneous, multi-country life-sciences data into trusted, reusable data products.
The role spans clinical trial data, real-world data (RWD), and omics — harmonising these into standardised, regulatory-grade, analysis-ready assets. Combines hands-on engineering on Azure and Databricks with technical leadership of a multidisciplinary team.
Requirements
Required (Must-Have)
8+ years in data engineering, with substantial Life Sciences / pharmaceutical experience.
Proven delivery of cloud data platforms on Azure and Databricks; familiarity with Microsoft Fabric.
Strong proficiency in Python and SQL, plus ETL/ELT orchestration (Azure Data Factory).
Hands-on experience with CDISC standards (SDTM, ADaM) and clinical data workflows.
Relational and non-relational stores: SQL Server, PostgreSQL, MongoDB.
Data governance, access control, and sensitive/anonymised data handling.
Team leadership and Agile delivery (Scrum, SAFe, Kanban). Preferred (Nice-to-Have)
OMOP CDM and real-world data standardisation experience.
Omics / bioinformatics data and large-scale scientific datasets.
Graph databases (Neo4j) and knowledge-graph modelling.
BI & visualisation: Power BI, Metabase, Streamlit.
Certifications (Preferred) Databricks Certified Data Engineer (Associate / Professional) Microsoft Certified: Azure Data Engineer / Fabric Analytics Engineer Associate Neo4j Certified Professional Professional Scrum Master (PSM I / II) Soft Skills
Cross-functional collaboration with scientific and business stakeholders.
Clear communication of technical concepts to non-technical audiences.
Multilingual capability for global study support (an asset). Team leadership and Agile delivery (Scrum, SAFe, Kanban)
Strong proficiency in Python and SQL
clinical data workflows
Pharmaceutical experience
ETL/ELT orchestration
SQL Server, PostgreSQL, MongoDB
Proven delivery of cloud data platforms on Azure and Databricks
8+ years in data engineering
Azure DataFactory
Data governance, access control, and sensitive/anonymised data handling
Hands-on experience with CDISC standards (SDTM, ADaM)
Microsoft Fabric
Preferred skills
OMOP CDM and real-world data standardisation experience
Omics / bioinformatics data and large-scale scientific datasets
BI & visualisation: Power BI, Metabase, Streamlit
Graph databases (Neo4j) and knowledge-graph modelling
