Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Medium: strong employer brand and broad data skills raise competition, but seniority and niche healthcare preference reduce density.
Medium: core data engineering skills transfer, but healthcare domain experience and pharma context increase specificity.
High: explicit 7+ years requirement plus mandatory Python, PySpark, AWS, SQL, and healthcare domain familiarity.
Lead and manage the data engineering team to deliver scalable data ingestion, integration, and automation solutions supporting data science and analytics products in healthcare/life sciences.
Design and implement data pipelines using Python, PySpark, and CI/CD workflows, ensuring data quality and adherence to FAIR principles throughout the data lifecycle.
Collaborate with architects, vendors, and stakeholders to ensure alignment with best practices and support end-user training for self-service data capabilities.
University degree in Informatics, Computer Sciences, Life Sciences, or related field.
7+ years of data engineering experience with a good understanding of healthcare or life sciences; Commercial experience is a plus.
Proven expertise in Python, PySpark, and R for ETL and BI development; strong skills in SQL (Oracle, MS SQL Server) and experience with AWS cloud data integration and DevOps.
Experience with ETL tools, BI platforms (e.g., Power BI), and familiarity with data architecture, modelling, Agile methodologies; Cloud database knowledge (Snowflake) is a plus.
Experienced in managing cross-functional data engineering teams delivering cloud-based data integration and automation solutions in healthcare/life sciences contexts.
Strong technical proficiency in Python, PySpark, R, SQL, DevOps, and AWS cloud environments with hands-on ETL and BI tool expertise.
Capable of aligning technical data engineering practices with enterprise data governance, metadata management, and FAIR data principles in global project setups.