Protocol Data Engineer

Hands-on protocol data engineering role supporting an FDA proof of concept for AI-assisted clinical trial protocol review.

• Implement protocol data structures and physical schemas for extraction, normalization, storage, retrieval, comparison, and reviewer-facing use covering ICH M11-aligned and historical protocols
  .
• Translate approved ICH M11
  , CDISC USDM
  , ontology, metadata, and terminology models into practical database structures and POC data-engineering components.
• Develop and support ETL/ELT pipelines and data transformations for protocol content and structured outputs, preserving source provenance
  , metadata, assumptions, validation status, and reviewer feedback.
• Collaborate with AI/prompt engineering and standards SMEs to support retrieval-augmented generation (RAG), semantic retrieval, indexing, evidence caching, and protocol comparison components.
• Support integration with FDA-furnished Elsa/HALO capabilities and FDA-approved data services
  , including PostgreSQL and/or HALO/Databricks
  , for evidence caching, retrieval, analytics, and dashboard-ready outputs.
• Build advanced SQL and data-access logic for protocol extraction, historical retrieval and comparison, reviewer dashboards, and traceability to source evidence.
• Execute data-focused quality assurance, reconciliation, validation, and regression testing, including checks for completeness, mapping consistency, retrieval quality, unsupported values, ambiguity, and reproducibility.
• Document data models, interfaces, transformations, mappings, implementation decisions, technical limitations, and operational considerations to support FDA review, knowledge transfer, and future expansion.
• Work closely with clinical protocol, data standards, architecture, interoperability, and AI/prompt engineering SMEs.
• Participate in FDA technical working sessions, POC demonstrations, validation activities, and iterative refinement based on reviewer feedback.
• Support implementation within FDA-provided platforms and approved data environments
  ; the base effort is a POC and does not require a separate production platform.
• Provide practical database engineering and data-quality expertise while relying on designated SMEs for clinical interpretation and standards governance.

• Extensive experience in database engineering
  , data architecture
  , information modeling
  , and analytics in complex enterprise environments.
• Strong expertise in relational and dimensional data modeling
  , metadata management
  , ETL/ELT design
  , data warehousing
  , and data quality
  .
• Advanced SQL skills, including PostgreSQL/PLpgSQL and/or Oracle PL/SQL
  ; SQL Server experience beneficial.
• Experience with AWS data services and cloud database platforms such as Aurora PostgreSQL
  , Redshift
  , S3
  , Glue
  , Lambda
  , and DMS
  .
• Experience integrating JSON/XML data and RESTful APIs
  ; working knowledge of Python and CI/CD practices beneficial.
• Federal health, clinical research, or regulated-environment experience preferred; familiarity with FISMA/NIST controls beneficial.

• PostgreSQL
• PLpgSQL
• Oracle PL/SQL
• SQL Server
• AWS
• Aurora PostgreSQL
• Redshift
• S3
• Glue
• Lambda
• DMS
• JSON
• XML
• RESTful APIs
• Python
• CI/CD
• Elsa/HALO
• HALO/Databricks
• ETL/ELT
• RAG
• FISMA
• NIST
• ICH M11
• CDISC USDM

• Remote

• Must be able to obtain a High-Risk Public Trust

• BS degree in Computer Science, Mathematics, Data Science or relevant technical field