As a Principal Backend Engineer, you will build and scale reliable backend systems that power customer intelligence and AI-driven workflows. You will take ownership of complex product challenges, transforming ambiguous customer needs into practical, production-ready solutions. Working primarily with Go and PostgreSQL, you will develop multi-tenant services that integrate data from CRMs, customer conversations, and support platforms. You will design intelligent AI workflows that deliver actionable insights while maintaining reliability, security, and auditability. Your work will help revenue teams and AI agents better understand customer accounts and automate meaningful business actions. In a fast-moving, product-led environment, you will collaborate closely with stakeholders, leverage AI-assisted development tools, and influence technical decisions from architecture through production deployment.
Backend Engineering and Product Development: Design, develop, and maintain customer-facing features within a multi-tenant backend architecture using Go and PostgreSQL, ensuring solutions are reliable, maintainable, and scalable.
Customer Data Integration: Connect and consolidate information from multiple sources, including CRM platforms, customer conversations, and support systems, to create a consistent and actionable view of customer accounts.
AI Workflow Engineering: Build production-grade AI workflows that analyze information, generate customer intelligence, and execute permission-based actions while maintaining traceable and auditable records.
LLM and Application Architecture: Determine which tasks should be handled by large language models and which require deterministic application logic, calculations, or persistent data storage to ensure accuracy and control.
Database Design and Optimization: Design efficient PostgreSQL schemas, troubleshoot complex queries, resolve performance bottlenecks, and optimize data access patterns to support growing workloads.
Reliability and Quality Assurance: Develop workflows that can be safely rerun, implement appropriate error-handling mechanisms, and monitor output quality to identify regressions and maintain consistent results.
Scalability and Performance: Ensure services remain responsive and dependable across customer workspaces ranging from approximately 100 to 100,000 accounts.
Infrastructure and Deployment: Support production operations across AWS and Docker-based environments, including enterprise installations and on-premises deployments.
End-to-End Ownership: Take responsibility for features throughout their lifecycle, from understanding customer problems and designing technical solutions to implementation, deployment, monitoring, and incident resolution.
Customer-Driven Improvement: Work closely with users and incorporate customer feedback to refine existing functionality, improve usability, and deliver measurable product value.
Rapid Delivery and Iteration: During the first 30 days, become familiar with the existing system by delivering approximately two to three fixes per week and one to two improvements to existing features.
Technical Leadership: Within three to six months, take ownership of a major product capability from initial requirements through production adoption. Potential areas include customer intelligence pipelines, customer success management agents, renewal automation, and expansion signals.
AI-Assisted Development: Use AI coding tools effectively to accelerate implementation while maintaining high standards for code quality, readability, testing, and long-term maintainability.