Data Engineer / Data Architect (Mid to Senior) (Top Secret Clearance Required) (Hybrid)
HybridQuantico, Maryland, United States
Job Summary
Design and implement scalable data architectures and secure cloud-native solutions within an Impact Level 5 environment. Develop and maintain data ingestion, transformation, and processing pipelines while engineering backend services and APIs. Engineer secure cloud-native data solutions supporting Controlled Unclassified Information (CUI) and collaborate with Government stakeholders to deliver mission-focused solutions. Generate synthetic datasets using Government-provided schemas when production data is unavailable and validate data quality through QA collaboration. Ensure compliance with security requirements, coding standards, and Agile development best practices. Requires Active Top Secret Security Clearance and experience with AWS, Azure, or Google Cloud.
Required Qualifications
- Active Top Secret Security Clearance
- Ability to obtain and maintain access required for the program
- Mid to Senior level experience in one or more of the following: Data Architecture, Data Engineering, Data Science, Backend Software Engineering, Cloud Engineering, Software Quality Assurance
- Experience designing and implementing enterprise data solutions
- Experience developing scalable ETL/ELT pipelines and data processing workflows
- Experience working within cloud environments such as AWS, Azure, or Google Cloud
- Experience developing backend applications, services, or APIs
- Experience with SQL and modern database technologies
- Experience using Git and CI/CD pipelines
- Strong troubleshooting, analytical, and problem-solving skills
- Excellent written and verbal communication skills
- Ability to collaborate with technical teams and Government customers in an Agile environment
Desired Qualifications
- Experience supporting Department of Defense or Federal Government programs
- Experience working with Controlled Unclassified Information (CUI)
- Experience supporting DoD Impact Level 5 (IL5) cloud environments
- Experience generating synthetic datasets for testing, development, or analytics
- Experience with Docker, Kubernetes, and Infrastructure as Code
- Experience supporting cloud-native architectures and DevSecOps practices
- Familiarity with machine learning, analytics, or AI-enabled data platforms
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