Data Engineer

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

Requirements

• 4+ years of experience in Data Engineering or Analytics Engineering
• Active DoD Secret Clearance (or higher)
• Proficient in SQL and Python
• 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
• Familiarity with AWS (Glue, S3, Lambda, Athena, EMR) and cloud-native data architectures

Preferred Qualifications

• Experience with FHIR, OMOP, or HL7 data standards
• Background in DoD, VA, or other federal health IT programs
• Knowledge of Responsible AI and NIST AI Risk Management Frameworks
• Familiarity with complex operational data systems or readiness analytics

Benefits

• Weekly Pay and Full Remote Flexibility
• Professional Growth through paid certifications and training
• 401(k) with 100% match up to 6%
• PTO, medical, dental, and vision insurance
• Life and disability insurance
• Home office equipment plan

About the Company

Rackner is a cloud-native software consultancy delivering solutions for startups, enterprises, and the public sector, enabling digital transformation through DevSecOps, AI/ML, and cloud-first innovation.