Senior Backend Engineer

Responsibilities

• Design and implement scalable backend services in Go that improve operator decision-making and response.
• Build high-performance APIs that power customer-facing workflows and internal platform capabilities.
• Model complex relationships in graph databases and optimize schemas and queries for speed and clarity.
• Implement event-driven systems (e.g., with NATS) to process real-time operational data streams.
• Develop and maintain automated tests to protect correctness, security, and performance as the system evolves.
• Build and operate microservices deployed on AWS, with an eye toward resilience and cost-aware scaling.
• Create and improve CI/CD pipelines that enable fast, secure delivery of new capabilities.
• Partner with cyber operations experts to translate domain needs into robust product features.
• Collaborate with frontend engineers on API contracts and data structures that are efficient and usable.
• Evaluate new technologies and patterns that could unlock better operational outcomes.

Requirements

Must Have:

• 5+ years of professional software development experience building backend (or full-stack) applications.
• Strong proficiency with Go (Golang) and have shipped production services with it.
• Experience working with graph databases (Neo4j, AWS Neptune, or similar), including schema design and query performance.
• Working knowledge of event-driven architectures and message queues/streaming systems.
• Experience building and maintaining APIs (REST and/or GraphQL).
• Experience with cloud infrastructure, ideally AWS, and understand cloud-native deployment patterns.
• Hands-on experience with containerization and microservices architectures.
• Strong foundation in secure coding practices and can reason about security tradeoffs.
• Bachelor’s degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.

Nice To Have:

• Delivered software in defense, security, or other mission-critical environments.
• Built systems for analysts, operators, or high-tempo users where speed and reliability matter.
• Integrated AI/ML capabilities into production systems (inference, pipelines, or tooling).
• Experience with large-scale data processing and analytics workloads.
• Contributed meaningfully to open-source projects.
• Used observability tooling (metrics, tracing, logging) to debug and improve production systems.