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 Originally posted on Himalayas