Data Engineer, Clinical Operations

What you'll do

• Design, build, and maintain scalable production-grade data pipelines for cross-study operations and specimen management.
• Develop and enhance data solutions to ensure interoperability and scalability across clinical R&D programs.
• Optimize platform components using Databricks Delta Lake, cloud-native parallel processing, caching, and partitioning.
• Implement robust ETL/ELT pipelines and data models using semantic modeling for complex life sciences datasets.
• Enforce data governance, manage metadata, and ensure end-to-end lineage using Databricks Unity Catalog.
• Build and deploy GenAI and NLP-driven applications to improve specimen traceability and operational efficiency.
• Develop and manage machine learning models at scale using Databricks Mosaic AI and MLflow.
• Provide technical guidance and mentorship to junior team members on Databricks and engineering best practices.

What we're looking for

• Must have at least 2 years of hands-on experience in Data Engineering, Analytics, and AI/ML in a cloud environment.
• Requires hands-on expertise with Databricks tools including Delta Lake, Unity Catalog, Workflows, Mosaic AI, and MLflow.
• Must possess strong proficiency in Python, SQL, Spark (including PySpark), and Generative AI frameworks.
• Requires experience with LLM architectures, RAG, prompt engineering, and agentic frameworks.
• Must have expertise in cloud-native data platforms, ETL/ELT pipeline design, data modeling, and semantic analytics for large datasets.
• Experience delivering production-grade GenAI applications and predictive models for clinical or research functions is required.
• A Databricks certification (e.g., Data Engineer Associate or Professional) is preferred.
• Knowledge of life sciences R&D and clinical trial operations is highly preferred.