高级临床数据工程师（拉美区）
Senior Clinical Data Engineer (LATAM)
我们正在墨西哥、巴西、阿根廷、哥伦比亚、智利和秘鲁扩展数据工程能力，并正在招聘一名高级临床数据工程师，以帮助建立这一新区域职能的基础。在此职位上，您将领导临床数据系统的开发与优化，推动数据标准化，构建验证框架，并设计高性能数据库和跨全球试验的数据管道。
高级临床数据工程师负责战略性地开发和优化临床数据系统，以支持全球试验中的监管合规性、高级分析和运营卓越。
该职位推动企业级数据标准化计划，并构建可扩展的验证框架，主动解决系统性数据问题。凭借在生命科学领域使用SAS编程的深厚专业知识，您将负责构建和维护模块化代码库，制定编码标准，并指导初级工程师。该职位还负责确保与GCP和FDA 21 CFR Part 11的监管一致性，建立完善的文档和审计追踪协议。
通过动态仪表盘和分析工具，您将提供有关试验表现和数据质量的可操作见解。作为生物统计学、临床运营和法规事务之间的战略联络人，您将帮助将临床需求转化为可扩展的技术解决方案，并领导安全、高性能数据库和ETL管道的设计，整合来自多种临床系统的数据。
该职位的核心职责包括但不限于：
数据标准化与映射：主导企业级数据映射策略的开发，将原始临床数据转换为标准化格式，用于高影响力分析。领导在各项目中采用和治理数据标准，以确保监管一致性和统一性。
数据质量保障：设计并实施稳健且可扩展的验证框架，主动检测并解决系统性数据问题。作为临床数据经理和跨职能团队的战略合作伙伴，推动全球试验中数据完整性的持续改进。
编程与脚本：使用SAS构建和维护先进的模块化代码库，以支持复杂的数据工程工作流程。我们高度优先考虑Python、R和SQL经验，因为我们继续在行业中利用AI工具。建立编码标准并进行指导
查看英文原文
We’re expanding our data engineering capabilities across Mexico, Brazil, Argentina, Colombia, Chile, and Peru, and we’re hiring a Senior Clinical Data Engineer to help build the foundation of this new regional function. In this role, you’ll lead the development and optimization of clinical data systems, drive data standardization, build validation frameworks, and design high‑performance databases and pipelines across global trials.
The Senior Clinical Data Engineer leads the strategic development and optimization of clinical data systems to support regulatory compliance, advanced analytics, and operational excellence across global trials.
This role drives enterprise-level data standardization initiatives, and architects scalable validation frameworks to proactively address systemic data issues. With deep expertise in SAS Programming in a Life Sciences setting, you'll be responsible for building and maintaining modular codebases, sets coding standards, and mentoring junior engineers. The role also oversees regulatory alignment with GCP and FDA 21 CFR Part 11, establishing robust documentation and audit trail protocols.
Through dynamic dashboards and analytics tools, you'll deliver actionable insights into trial performance and data quality. As a strategic liaison across Biostatistics, Clinical Operations, and Regulatory Affairs, you'll help translate clinical requirements into scalable technical solutions and leads the design of secure, high-performance databases and ETL pipelines integrating data from diverse clinical systems.
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.
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