Systems Engineer

The candidate will design, develop, and integrate distributed data architecture solutions with a strong emphasis on data analysis, AI/ML workflows, and automation. They will build prototypes and proof-of-concept implementations to support experimentation, integration, and capability development across shared testbeds and with partner organizations. This role supports Department of War (DoW) modernization initiatives focused on secure data sharing across multiple platforms and domains in distributed and cloud environments. The candidate will help research, evaluate, and prototype emerging technologies in data engineering, AI/ML, and observability to advance program objectives.

• Strong background in data analysis, identifying trends, drawing actionable insights, and evaluating data interoperability from diverse sources to support key decisions
• Experience designing and implementing distributed data architectures, including publish/subscribe messaging systems (e.g., NATS) and robust Extract, Transform, Load (ETL) pipelines
• Proficiency with high-performance database technologies (e.g., PostgreSQL, MongoDB, ClickHouseDB)
• Hands-on development experience with Python, Java, and C/C++ for high-performance data processing, analytics, and system components
• Deep understanding of observability and visualization stacks, including Grafana for dashboards, Prometheus, and VictoriaMetrics for monitoring large-scale distributed systems
• Ability to analyze complex systems, identify performance bottlenecks, debug issues, and implement efficiency improvements
• Experience gathering and analyzing system requirements, mapping them to capabilities, identifying gaps, and developing risk mitigation strategies
• Strong team collaboration using Agile methodologies, with the ability to work independently with minimal supervision
• Familiarity with DevSecOps pipelines, practices, and tools
• Experience or strong interest in network emulation and testbed environments

Preferred Skills

• Active DoW Secret or TS/SCI Clearance
• Knowledge of architecture frameworks such as DoDAF, applied across multi-domain and cloud environments
• Understanding of Common Intelligence Picture (CIP) and Common Operational Picture (COP) concepts
• Expertise in cloud-native infrastructure and automation using Kubernetes, Ansible, and Terraform for deploying and managing scalable environments
• Additional experience with containerization (Docker / Podman), infrastructure-as-code, and observability tools (Loki, Fluent Bit, OpenTelemetry)
• Proficiency in building AI-powered analytics platforms, including natural language querying, automated reporting, root-cause analysis, cross-run comparisons, and actionable recommendations
• Hands-on experience with AI/ML workflows, and integration of AI models into data pipelines and decision-support systems
• Experience with Open Mission Systems (OMS) and Universal Command and Control Interface (UCI)
• Strong UI/UX practices for data-driven dashboards and applications
• Proficiency in designing and optimizing low-latency distributed systems