Protocol Data Engineer

A P Ventures LLC is seeking a Protocol Data Engineer to support an FDA proof of concept for AI-assisted clinical trial protocol review. This remote role focuses on turning approved protocol, standards, and terminology models into reliable, scalable database structures, transformations, and retrieval workflows inside FDA-approved environments.

You will work hands-on with protocol data structures and evidence traceability, collaborating with clinical protocol, data standards, architecture, interoperability, and AI/prompt engineering SMEs throughout development, validation, and iterative refinement based on reviewer feedback.

Responsibilities

• Implement protocol data structures and physical schemas to enable extraction, normalization, storage, retrieval, comparison, and reviewer-facing use across both 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 on 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.
• Perform 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 driven by reviewer feedback.
• Support implementation within FDA-provided platforms and approved data environments, with the base effort limited to a POC that does not require a separate production platform.
• Contribute database engineering and data-quality expertise while relying on designated SMEs for clinical interpretation and standards governance.

Requirements

• 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 is 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 is beneficial.
• Federal health, clinical research, or regulated-environment experience is preferred; familiarity with FISMA/NIST controls is beneficial.

Technologies

• 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

Location

• Remote

Clearance

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

Education

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