Back
Tier-1 employer and metro location increase applicant density, balanced by niche data engineering and life-science requirements.
Data engineering skills transferable but healthcare/life-sciences experience increases fit sensitivity.
Explicit 6+ years requirement plus mandatory Python, PySpark, AWS and healthcare domain skills.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead and manage a data engineering team to design and build scalable data ingestion and integration solutions supporting data science and reporting analytics.
Ensure data quality, apply data management principles, manage metadata, and enforce FAIR data principles across projects.
Collaborate with cross-functional teams, automate data pipelines using Python and CI/CD, and support end-user training and self-service data capabilities.
6+ years of experience in data engineering with good understanding of healthcare or life sciences; Commercial experience is a plus.
University degree in Informatics, Computer Sciences, Life Sciences, or a related field.
Proficiency in Python, PySpark, R, SQL (Oracle, MS SQL Server), DevOps, AWS cloud data integration, and third-party ingestion tools.
Experience with ETL tools (e.g., Alteryx), BI platforms (e.g., Power BI), data architecture, modelling, and Agile methodologies in global projects.
Experienced leader capable of managing teams and collaborating with architects and vendors to implement best practices in data engineering.
Strong background in healthcare or life sciences data with ability to handle complex data governance and quality requirements.
Skilled in building automated scalable data pipelines and enabling data self-service within a global project environment.