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 proficiency in Python and SQL, including object-oriented programming, complex query development, data transformation, performance tuning, automated testing, debugging, and reusable framework development. Hands-on experience implementing scalable batch and streaming pipelines using technologies such as Apache Spark, Databricks, Airflow, Kafka, or comparable distributed data-processing and orchestration tools. Experience with cloud data engineering services, preferably Amazon Web Services, including object storage, serverless compute, data integration, cataloging, messaging, monitoring, identity, and access-management capabilities. Experience designing data-lake, lakehouse, and warehouse solutions using platforms such as Snowflake or Databricks, including partitioning, file formats, schema evolution, workload optimization, and cost-performance management. Strong knowledge of dimensional, relational, canonical, and domain-oriented data modeling, with experience creating curated data layers, reusable data products, and application-ready datasets. Experience integrating structured, semi-structured, and unstructured data through extract-load-transform and extract-transform-load patterns, application programming interfaces, event streams, files, and database connectors. Experience implementing data-quality controls, reconciliation, schema validation, metadata management, lineage, observability, alerting, error handling, restartability, and service-level monitoring. Proficiency with software engineering and DataOps practices, including Git, branching strategies, code reviews, unit and integration testing, continuous integration and continuous delivery, environment promotion, and release automation. Understanding of secure data-engineering patterns, including role-based access, encryption, secrets management, data masking, privacy controls, audit logging, retention, and regulated-data handling. Strong analytical, production troubleshooting, root-cause analysis, technical documentation, and communication skills, with the ability to deliver independently and collaborate across global product, platform, architecture, analytics, and clinical teams. PREFERRED QUALIFICATIONS Advanced degree in Data Engineering, Computer Science, Data Science, Information Systems, Engineering, or a related discipline. Experience engineering data for enterprise artificial intelligence and machine learning platforms, including feature pipelines, model-training and evaluation datasets, embeddings, vector stores, semantic data preparation, retrieval-augmented generation, and model monitoring. Experience with medallion or layered lakehouse architectures, open table formats such as Delta Lake, Apache Iceberg, or Apache Hudi, and distributed processing of high-volume datasets. Experience building reusable ingestion and transformation frameworks, metadata-driven pipelines, data contracts, schema registries, and self-service data capabilitie