We are looking for a Senior/Lead Full-Stack Engineer with a strong backend and product-integration focus, who will also play a key role in shaping engineering standards and development practices across the team.
This person will take ownership of turning business workflows and early MVPs into reliable, production-ready applications — and will establish the patterns and guidelines others follow, using our agreed engineering stack.
You move comfortably across product UI, backend APIs, database design, integrations, and production readiness — and you take real satisfaction in building not just features, but the foundation others build on.
Work split: ~70% backend/architecture/integrations, ~30% frontend/product workflows. This is not a people management role — impact comes through technical leadership: decisions, standards, and code.
Lead architecture decisions for our Internal Operational Platform and connected tooling, and define the engineering standards and code quality practices the team follows
Build and maintain full-stack applications (Java/Spring Boot + React/TypeScript), turning vibe-coded MVPs and prototypes into clean, production-ready services with typed APIs, proper auth, persistence, and observability
Design PostgreSQL schemas and Liquibase migrations, and integrate BigQuery reads and publish layers
Champion OpenAPI-first development and typed frontend API clients
Own backend observability (Actuator, Prometheus, structured logs)
Partner with business stakeholders to preserve product intent while raising technical quality
Backend: Java 21, Spring Boot 3.x, REST/OpenAPI contract-first development, PostgreSQL + Liquibase, Spring Security, and observability tooling (Actuator, Prometheus). Bonus if you've defined backend architecture standards, worked in multi-module Maven projects, or integrated legacy systems without rewriting them.
Frontend: React + TypeScript, API-driven UI, server-state management (e.g. TanStack Query), and a good instinct for loading/error/empty states and practical workflow UIs.
Data & Integration: Experience with BigQuery or a similar warehouse, and comfort reasoning about versioned data, source-of-truth boundaries, freshness, and cache/fallback behavior. Nice to have: Google APIs (Sheets, Drive, BigQuery), workflow automation tools like n8n.
AI-Assisted Development: Hands-on experience building with LLM tools and APIs (OpenAI, Claude/Anthropic, Cursor, Replit AI, or similar), and the judgment to evaluate, refactor, and productionize AI-generated code into a clean, maintainable stack.
