Emergence Software - Principal Engineer

• Must-haves • 6-10 years production software engineering with measurable outcomes: shipped features, reduced latency, improved reliability, or scaled systems 10x+. • Deep hands-on Node.js/TypeScript backend experience in production: debugged performance issues, optimized database patterns, shipped measurable improvements. • Designed and shipped two or more distributed systems handling production load that remain operational without constant firefighting. • Led a team of 5+ engineers as technical lead for 12+ months: owned code review, delivery quality, architectural direction. • Owned a critical production incident end-to-end: diagnosed root cause, deployed hotfix, shipped prevention, led post-mortem. • Communicated architectural trade-offs clearly to non-technical stakeholders: explained cost/latency/complexity implications and made defensible recommendations they understood. • Python engineering depth sufficient to review architectural decisions and contribute to automation layer integration design discussions. • Nice-to-haves • Scaled systems from 1k to 100k+ requests per second or single-region to multi-region deployment. • Track record of reducing technical debt measurably: refactored services, reduced incidents by 50%+, or cut deployment time by 70%+. • Experience with Kubernetes, containerization, or infrastructure-as-code like Terraform or CloudFormation. • You identify gaps in backend architecture without waiting for a roadmap and propose solutions with effort estimates. You've expanded your scope by owning infrastructure problems you didn't have to own, like building a new alerting system or refactoring a critical service. You define technical roadmaps based on business impact and system health. You advocate for better solutions authored by junior engineers when you believe they're superior to yours. You actively transfer knowledge through documentation, workshops, and pairing to reduce single points of failure. You define quality standards other teams adopt, back them with data, and refuse to merge flawed code under deadline pressure. You review architecture with the same rigor as code, catching edge cases in distributed systems and measuring reliability improvements before shipping.