Principal Clinical Datawarehouse Programmer
New
USFull-TimePrincipal
Salary$125,000 to $165,000 annually
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLETLR
Requirements
- Bachelor’s degree in Computer Science, Software Engineering, or related scientific discipline
- 5+ years of experience in ETL data pipeline development and clinical data warehousing
- 5+ years of strong SQL and relational database experience
- 2+ years of experience working within SDLC environments
- 3+ years of experience with clinical trial data in pharmaceutical or biotechnology settings
- Experience with APIs and integration of clinical or enterprise data systems
- Strong understanding of clinical data standards such as CDISC SDTM, SDTMIG, and CDASH (preferred)
- Hands-on experience with Oracle clinical data platforms such as LSH, DMW, CDA, and ODI
- Experience with BI tools such as OBIEE, Spotfire, or Power BI is a plus
- Proficiency in programming languages such as Python, SAS, or JavaScript is highly desirable
- Strong analytical, debugging, and problem-solving skills in complex data environments
- Ability to collaborate effectively with technical and non-technical stakeholders across clinical and IT functions
Responsibilities
- Translate business and clinical requirements into ETL data pipeline design specifications following SDLC processes
- Design, build, test, and deploy scalable ETL pipelines, validation checks, and clinical data models
- Develop and maintain high-quality data workflows using SQL and relevant programming tools such as Python or R
- Assemble and integrate large, complex clinical datasets from multiple structured and semi-structured sources
- Ensure data accuracy, quality, and compliance through validation, monitoring, and testing of outputs
- Maintain and optimize ETL pipeline performance while enforcing data governance, security, and data protection standards
- Support troubleshooting, debugging, and resolution of data pipeline issues in production environments
- Collaborate with cross-functional stakeholders to address data needs and support clinical and analytical use cases
- Develop and support infrastructure for efficient data processing and downstream analytics applications
- Ensure data readiness for reporting, clinical studies, regulatory submissions, and business intelligence use
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