Senior Associate, AI and Analytics Data Engineer

ROLE SUMMARY Clinical development & Operation is a critical domain where scientific innovation is translated into evidence that supports new medicines and improves outcomes for patients and trial participants globally. It operates at the intersection of clinical operations, data science, digital platforms, external partners, and global regulatory expectations, requiring solutions that are scalable, compliant, user-centered, and trusted across diverse stakeholders. Digital platforms, data products, and AI-enabled capabilities are increasingly essential to improving clinical trial planning, execution, monitoring, and decision-making. This role helps deliver responsible technology solutions that strengthen data quality, reduce operational complexity, support audit readiness, and enhance the experience of clinical teams, investigators, and participants while keeping participant safety at the center of the domain   As a Sr. Associate, AI and Analytics Data Engineer, you will be part of a global team responsible for engineering the trusted, scalable, and governed data foundation for a major AI-driven platform serving the Clinical Development and Operations domain. You will design and build reusable data products, pipelines, and processing frameworks that integrate complex clinical, operational, and external data; enable advanced analytics, machine learning, and generative AI use cases; and meet enterprise expectations for security, privacy, quality, lineage, reliability, and regulatory compliance.   Join our diverse team in making an impact on patient health through the application of cutting-edge technology and collaboration. ROLE RESPONSIBILITIES • Design, build, test, and operate scalable batch and streaming data pipelines that ingest, standardize, and integrate clinical, operational, scientific, and external data for a major AI-driven clinical platform. • Develop curated, reusable, and well-documented data products and feature-ready datasets that enable analytics, machine learning, generative AI, and agentic AI use cases. • Implement robust data modeling, transformation, metadata, lineage, and master/reference data patterns across data lake, lakehouse, warehouse, and application-serving layers. • Engineer automated data-quality controls, reconciliation, observability, alerting, and recovery capabilities to support trusted and reliable AI outcomes. • Optimize pipeline performance, scalability, availability, and cloud cost while supporting high-volume and diverse clinical data workloads. • Apply DataOps, continuous integration and continuous delivery, infrastructure-as-code, automated testing, and version-control practices across development and production environments. • Partner with clinical domain experts, data scientists, machine learning engineers, architects, security, privacy, and platform teams to translate AI use-case needs into production-grade data solutions. • Embed security, privacy, consent, access controls, auditability, and applicable regulatory requirements into data-engineering designs and delivery practices. • Troubleshoot complex data and production issues, perform root-cause analysis, and drive sustainable remediation and operational improvements. • Contribute to engineering standards, reusable frameworks, technical documentation, code reviews, and mentoring that improve delivery consistency and platform adoption across teams. BASIC QUALIFICATIONS   • Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, Information Systems, Engineering, or a related discipline. • 3+ years of hands-on data engineering or software engineering experience designing, building, deploying, and supporting production-grade data pipelines, data platforms, or data-intensive applications. • Advanced