Rackner is looking to modernize secure data infrastructure for a critical defense health program to enable real-time insights that support care delivery, operational readiness, and mission decision-making. The job involves designing and scaling data pipelines to transform raw information into actionable intelligence, ensuring interoperability and powering analytics across large-scale, distributed environments.
Requirements
- Proficient in SQL and Python, with experience building modular, version-controlled transformations
- Hands-on with Apache Airflow, Apache Spark, dbt, and data lake frameworks (Iceberg, Trino, Athena)
- Strong understanding of ETL/ELT design, data modeling, and governance in distributed systems
- Familiar with AWS (Glue, S3, Lambda, Athena, EMR) and cloud-native data architectures
- Experience with FHIR, OMOP, or HL7 data standards
- Knowledge of Responsible AI and NIST AI Risk Management Frameworks
- Familiarity with complex operational data systems or readiness analytics
Responsibilities
- Build and maintain end-to-end Ingest → Transform → Expose pipelines using Airflow, Spark, dbt, and Iceberg.
- Ingest and normalize structured and unstructured data (HL7, FHIR, PDF, JSON) for analytics and AI/ML use cases.
- Map datasets to FHIR and OMOP standards to enable interoperability and decision support.
- Implement schema versioning and governance to ensure traceability and audit-ready lineage.
- Collaborate with DevSecOps and Data Science teams to deliver AI-ready datasets for predictive analytics and readiness forecasting.
- Optimize data performance across distributed environments while ensuring compliance with DoD Responsible AI and NIST AI Risk Management frameworks.
Other
- Active DoD Secret Clearance (or higher)
- 4+ years of experience in Data Engineering or Analytics Engineering
- Excellent collaborator with experience in cross-functional environments (DevSecOps, Data Science, Security)
- Background in DoD, VA, or other federal health IT programs
- Remote Flexibility
