Clinical Data Engineer

• Data Modeling & Warehousing: Apply data modeling best practices to transform source data into governed by common data models. Incorporate data warehouse concepts including star schemas to support dashboard reporting and implement data lineage strategies to enable auditability and inspection readiness.
• SQL & Performance Engineering: Develop intermediate T-SQL stored procedures and leverage window functions, common table expressions, derived tables, and dynamic T-SQL. Optimize and tune complex queries and data processing workflows to ensure performance and scalability.
• Think like a software developer by refactoring for reuse, applying design patterns, encapsulating logic, and minimizing side effects.
• Data Standardization & CDISC Governance: Lead enterprise-level mapping strategies that transform raw clinical data into standardized CDISC-compliant formats. Maintain strong knowledge of SDTM and ADaM structures and ensure consistent governance across studies.
• Data Quality & Validation: Design scalable validation frameworks that proactively detect systemic data issues. Prepare, correct, modify, and analyze large, complex datasets using advanced analytical techniques to ensure integrity and traceability.
• Regulatory Compliance & Documentation: Maintain a strong understanding of GCP, FDA 21 CFR Part 11, and evolving regulatory guidance as they pertain to data curation deliverables. Establish documentation standards, audit trail governance, and inspection-ready processes.
• Reporting & Analytics Enablement: Enable real-time dashboards and operational KPIs by delivering curated, high-quality datasets to analytics platforms. Support internal and external stakeholders through innovative tools and modern engineering solutions.

Minimum Requirements

• Bachelor’s degree in life sciences, statistics, data engineering, computer science, or related field, or equivalent experience.
• Minimum 5 years of experience in data engineering or a related discipline, preferably within clinical research or a CRO environment.
• ​​​​Intermediate proficiency in SQL and Python; working knowledge of SAS and R • Strong understanding of clinical trial processes, protocols, CRF data, and CDISC standards (SDTM/ADaM).
• Demonstrated ability to mentor team members and collaborate cross-functionally.
  • Strong analytical, organizational, and communication skills.