5+ years of data engineering experience, emphasizing cloud solutions
U.S. Citizenship and active Secret clearance required
Experience in defense or government environments involving classified data
Proficiency in Python for data workflows
Hands-on experience with AWS-based engineering, knowledge of Azure/GCP is a plus
Strong understanding of data modeling and ingestion frameworks
Experience with Neo4j and relational databases
Responsibilities
Design and build data ingestion and processing pipelines for AI applications
Develop and maintain Python data workflows, ensuring quality through testing
Collaborate with cross-functional teams of engineers, data scientists, and designers
Implement CI/CD pipelines to enhance deployment integration
Work across multi-cloud environments primarily using AWS
Utilize Docker for scalable deployments
Optimize data systems for generative AI applications
Benefits
Opportunity to work on high-impact AI platform initiatives
Fast-paced environment with strong technical mentorship
Structured development programs for continuous learning
Collaborative culture prioritizing innovation and diverse perspectives
Experience with globally distributed teams and modern engineering practices
Full Job Description
About the job Data Engineer
Job Title: Data Engineer - AI Platform
Location: Boston, Chicago, New York City, San Francisco, Silicon Valley, Washington DC
Number of openings: 1
Experience: 5+ years of professional experience as a data engineer
Salary: $165,000 - $175,000
Clearance: Secret (prefer TS)
Position Summary
We are hiring a skilled Data Engineer to contribute to the development of a sophisticated AI-powered platform that supports advanced, data-driven solutions. This role offers the opportunity to work on cutting-edge initiatives involving generative AI, large-scale data systems, and cloud-native architectures.
You will partner with a multidisciplinary team of engineers, data scientists, and product stakeholders to design and implement scalable data infrastructure. The primary focus will be enabling platform capabilities tailored for government and defense-related environments.
Key Responsibilities
Design and build robust data ingestion and processing pipelines to support AI and GenAI applications
Develop and maintain high-quality Python-based data workflows, including testing and validation
Collaborate with cross-functional teams including software engineers, data scientists, and UI/UX designers
Implement and enhance CI/CD pipelines (including GitHub Actions) to streamline deployment and integration processes
Work across multi-cloud environments (AWS primarily; exposure to Azure and GCP is beneficial)
Utilize containerization technologies (Docker) to support scalable and portable deployments
Contribute to the development and optimization of data systems used by generative AI applications
Ensure performance, reliability, and scalability of data pipelines through monitoring and diagnostics
What You'll Experience
Opportunity to work on high-impact AI platform initiatives
A fast-paced, performance-driven environment with strong technical mentorship
Continuous learning through structured development programs and real-world problem solving
A collaborative culture that values innovation, ownership, and diverse perspectives
Exposure to globally distributed teams and modern engineering practices
Competitive compensation along with a comprehensive benefits package
Required Qualifications
U.S. Citizenship is required
Prior experience supporting defense or government environments, including handling classified or sensitive data
Active Secret clearance required (Top Secret or higher preferred)
5+ years of experience in data engineering, with a strong focus on cloud-based solutions
Technical Skills & Expertise
Strong proficiency in Python for data engineering and pipeline development
Hands-on experience with AWS-based data engineering (experience with Azure or GCP is a plus)
Deep understanding of data modeling, data ingestion frameworks, and pipeline architecture
Experience working with knowledge graphs, particularly using Neo4j, and familiarity with relational databases
Expertise in writing clean, maintainable, and testable code, including automation and error handling best practices
Practical experience with Docker and containerized workflows
Strong background in data pipeline performance tuning, monitoring, and troubleshooting
Excellent analytical and problem-solving capabilities
Effective communication skills and ability to collaborate within cross-functional teams
