Key skills
PythonSQLETL/ELT pipelinesREST APIsData modelingData lifecycle managementAWSAzureDockerKubernetesData validationData monitoringData alertingCybersecurity data platformsOwnership mindsetAIMLETLELTData EngineeringAnalyticsAgileCollaboration
About this role
Revolutional delivers advanced technology solutions and mission support to federal agencies, focusing on modern capabilities like AI/ML and IT modernization. The Data Engineer will be responsible for building and maintaining data pipelines that support cybersecurity operations, ensuring data quality and reliability in a production environment.
Responsibilities:
- Design, develop, and maintain ETL/ELT pipelines for secure data ingestion, transformation, and reporting
- Build and manage REST APIs and data ingestion services across systems
- Write production-grade code using Python and/or SQL to support data processing and automation
- Implement data validation, monitoring, and alerting to ensure data quality and reliability
- Identify and resolve schema changes, data inconsistencies, and pipeline failures
- Work with structured and unstructured data across multiple sources
- Leverage cloud platforms (AWS, Azure) for storage, compute, and data services
- Deploy and manage data workloads in containerized environments (Docker, Kubernetes)
- Support integration of data pipelines into broader DevSecOps and automation workflows
- Contribute to security-focused data initiatives, including data protection and risk analysis support
- Collaborate with engineering and security teams to support operational and analytical use cases
Requirements:
- Bachelor's degree (or equivalent experience)
- 3–7+ years of experience in data engineering or related hands-on development roles
- Ability to obtain and maintain a Public Trust clearance
- Strong programming skills in Python and/or SQL
- Experience building and maintaining ETL/ELT pipelines
- Experience developing and managing REST APIs and data ingestion processes
- Experience designing data models, schemas, and lifecycle management approaches
- Experience working with cloud platforms (AWS, Azure)
- Experience with containerization technologies (Docker, Kubernetes)
- Experience implementing data quality frameworks (validation, monitoring, alerting)
- Strong ownership mindset for pipeline reliability and data quality
- Hands-on problem solver who can debug and fix issues independently
- Ability to work in fast-moving environments with evolving requirements
- Strong attention to detail in data accuracy and validation
- Effective collaboration with engineering and security teams
- Experience supporting Department of Veterans Affairs or similar federal programs
- Experience working with cybersecurity data or risk analytics platforms
- Exposure to AI/ML-enabled data workflows or automation use cases
- Experience with streaming or near real-time data pipelines
- Experience working in DevSecOps or Agile environments