Senior Clinical Data Engineer (LATAM)

Essential functions of the job include but are not limited to:

Data Standardization & Mapping:

Spearhead the development of enterprise-level data mapping strategies that transform raw clinical data into standardized formats for high-impact analytics. Lead the adoption and governance of data standards across programs to ensure regulatory alignment and consistency.

Data Quality Assurance:

Design and implement robust, scalable validation frameworks that proactively detect and resolve systemic data issues. Serve as a strategic partner to Clinical Data Managers and cross-functional teams, driving continuous improvement in data integrity across global trials

Programming & Scripting:

Architect and maintain advanced, modular codebases using SAS to support complex data engineering workflows. Python, R, and SQL experience is highly preferred as we continue to leverage Ai tools across the industry. Establishing coding standards and mentoring junior engineers in automation, reproducibility, and performance optimization. Example use cases includes edit checks, reconciliations, exception listings, programmed protocol deviations, resource projections based on site data entry volume

Regulatory Compliance & Documentation:

Lead compliance initiatives to ensure all data systems and workflows meet GCP, FDA 21 CFR Part 11, and evolving global regulatory requirements. Define documentation protocols and oversee audit trail governance to support inspection readiness and transparency.

Reporting & Visualization:

Develop and operationalize dynamic dashboards and analytics tools that provide real-time insights into data quality, trial progress, and operational KPIs. Translate complex datasets into actionable intelligence for clinical and regulatory stakeholders.

Collaboration & Cross-Functional Support:

Act as a strategic liaison between Clinical Data Engineering and Biostatistics, Clinical Operations, and Regulatory Affairs. Translate clinical and scientific requirements into scalable technical solutions that support study execution and data delivery.

Database Design & Optimization:

Lead the design and optimization of secure, high-performance relational databases and data lakes. Ensure infrastructure scalability, query efficiency, and data governance for large-scale clinical datasets.

Qualifications:

Minimum Required:

• Bachelor’s Degree in a relevant field (life sciences, statistics, data management, clinical operations) or relevant/equivalent combination of education, training, and experience
• Minimum 6 years experience in clinical monitoring, clinical trial management or equivalent
• Advanced programming and automation skills; database design; dashboard development; CDISC governance
• Professional working proficiency in English

Other Required:

• Highly effective oral and written communication skills with the ability to communicate effectively with project team members
• Excellent organizational and time management skills
• Ability to work in a team or independently as required
• Demonstrated ability to learn how to extract pertinent information from protocols, electronic study data systems and clinical systems
• Proficiency in statistical analysis and data monitoring tools.
• Detail-oriented with strong analytical and problem-solving skills
• Demonstrated experience with integrated risk planning & management
• Ability to mentor junior team members

Preferred:

• CRO experience as a Clinical Data Engineer or Programmer
• Automation and Ai Agent Deployment in Regulated Environments

Skills:

• Strong analytical and problem-solving skills with attention to data quality and integrity.
• Ability to work with large, complex datasets from multiple sources.
• Effective communication skills to collaborate with cross-functional teams.
• Knowledge of data governance, privacy, and security best practices in clinical research.

Competencies:

• Collaboration: Works effectively with clinical, statistical, and technical teams to align data strategies with study goals.
• Accountability: Takes ownership of data engineering deliverables and ensures timely, high-quality outputs.
• Adaptability: Thrives in a fast-paced, evolving environment with shifting priorities and timelines.
• Compliance-Oriented: Maintains a strong focus on regulatory compliance and data traceability.