Clinical Data Systems Analyst

Responsible for developing, maintaining, and optimizing clinical data systems and data warehouses. Uses SAS programming, SQL, and clinical data management expertise to integrate, transform, validate, and manage clinical trial data. Ensures data accuracy, integrity, and compliance with CDISC standards while supporting clinical research operations and reporting.

• Bachelor's degree in Computer Science, Information Technology, Data Science, Statistics, Bioinformatics, or a related field.
• Priority is programming and data warehousing, applied within a clinical environment.
• Needs strong SAS programming experience.
• Needs intermediate-to-advanced SQL.
• Must have data warehouse development and maintenance experience, including clinical database management.
• Still needs solid understanding of CDISC, CDASH and SDTM.
• Needs real clinical trial data experience. Generic SAS or SQL backgrounds without it won't work.
• External data acquisition applies here too: vendor data, SFTP transfers, and integration and transformation of that data.
• Builds database schemas and edit checks, and maintains studies in the CDR across the study lifecycle.
• AI programming/automation exposure is a preferred plus.
• If choosing between a strong clinical systems/CDISC candidate and a strong SAS/SQL/warehouse candidate, the manager picks the SAS/SQL/warehouse one for this role.
• Develop and maintain clinical data warehouse databases and data structures.
• Perform SAS programming and advanced SQL queries for clinical data processing and analysis.
• Design and maintain database schemas, edit checks, and clinical data repositories (CDR).
• Integrate external clinical data from vendors using SFTP transfers and other data acquisition methods.
• Perform data extraction, transformation, validation, and loading (ETL).
• Apply CDISC, CDASH, and SDTM standards to clinical trial datasets.
• Monitor data quality, troubleshoot database issues, and ensure data integrity.
• Maintain clinical databases throughout the study lifecycle.
• Collaborate with clinical data management, programming, and IT teams.
• Support automation and AI-based programming initiatives to improve data workflows.