Senior Backend Engineer
Team8
Description
Who We Are & What You’ll Own
Fig leads the Security Operations Resilience movement – keeping detection and response working through constant change. Fig finds and fixes broken security flows across the SecOps stack, and enables security teams to quickly design, simulate, and deploy planned initiatives.
As Backend Engineer, you’ll own a meaningful backend domain within a fast-growing SecOps platform. You’ll work hands-on across design, implementation, and production, taking responsibility for systems that are already live and scaling. This position combines deep technical execution with real ownership, allowing you to influence architectural decisions and raise the bar on how we build secure, reliable backend systems.
What You’ll Be Doing
- Own the end-to-end lifecycle of core backend services, from initial architectural design to deployment and production monitoring.
- Design and implement scalable, low-latency APIs and microservices that support complex data flows and high-concurrency user demands.
- Develop sophisticated data schemas and optimize database performance to ensure data integrity and system responsiveness under heavy load.
- Lead the integration of third-party security protocols and internal identity management systems, maintaining a “security-first” approach to development.
- Ship high-quality, production-ready code in rapid iteration cycles, consistently meeting aggressive milestones without compromising on system stability.
- Influence the technical roadmap by identifying architectural bottlenecks and proposing modern, cost-effective solutions to improve system throughput.
- Collaborate across the engineering team to establish best practices for testing, CI/CD, and asynchronous documentation.
Requirements:
Experience: 4+ years in backend-heavy roles, preferably in Cybersecurity or SaaS environments
Backend: Python (required), Node.js (nice to have)
Data: PostgreSQL, Neo4j (strong advantage)
Infrastructure: AWS or GCP, Kubernetes, Docker, Terraform
AI Systems: Experience building production-grade AI agents (e.g., LangGraph / LangChain workflows), including LLM orchestration, tools integration, and modular architectures.