David Menoyo Ros

Senior Full-Stack & AI Engineer with 7+ years building end-to-end systems — from high-throughput backends and event-driven APIs to modern frontend applications and production-ready AI pipelines. My core stack gravitates toward Node.js, TypeScript and Python across the full backend spectrum, Next.js and React on the frontend, and relational or document databases — PostgreSQL for structured data, MongoDB when flexibility matters — but always shaped by what the problem actually demands. Beyond full-stack, a strong track record in data engineering at institutional scale: 3 years on the European Central Bank (ECB)'s core data platform, and increasingly applied to AI/LLM integration — agentic workflows, structured outputs, and scalable AI pipelines.

Key highlights:

• Real-Time Backend Systems: Built a real-time monitoring and data-processing engine from scratch using Node.js, designed around the Event Loop to handle concurrent data streams with minimal latency. Deep understanding of async queues, non-blocking I/O and performance bottlenecks at the application layer.
• API Design & Scalability: Designed and maintained REST APIs integrated with both SQL and NoSQL databases (including MongoDB and PostgreSQL) serving high-traffic production environments. Strong focus on maintainability, clean architecture and DDD principles.
• Observability & Reliability: At the European Central Bank's SPACE platform, I led the implementation of high-performance data pipelines processing 400M+ financial time series, with strict requirements around monitoring, traceability and system reliability — where engineering quality is non-negotiable.
• Cloud & Infrastructure: Hands-on with Docker, Kubernetes, AWS and CI/CD pipelines. Comfortable owning the full lifecycle of a backend service from local development to production deployment.
• AI Integration: Currently integrating LLM APIs (Anthropic, OpenAI) directly into product features using agentic workflows and Spec-Driven Development — treating AI as an architectural layer, not just a tool. My own engineering workflow is AI-native: I use Claude Code/OpenCodeand custom MCP tooling daily to ship faster without cutting corners on quality.

I work well in remote-first, international teams (currently at a German company, with experience delivering for European and Japanese enterprise clients). I value pragmatism, code reviews, good documentation and keeping complexity where it genuinely earns its place.

My approach is to build software that is not only functional but resilient, maintainable and observable in production.

Engineering Principles:

• Performance-first mindset: I diagnose memory leaks and Event Loop bottlenecks in Node.js services, using profiling tools (Clinic.js, Artillery) to ensure systems stay responsive under load — critical for real-time or high-traffic backends.
• Testing as a safety net: Strong advocate for integration and E2E tests (Playwright, Jest) with meaningful coverage. Code that ships without tests is code that surprises you at 2am.
• Clean architecture & DDD: I apply Domain-Driven Design and SOLID principles to keep services modular and easy to evolve — avoiding the monolith trap even in fast-moving teams.
• Infrastructure as code: Docker + Kubernetes + CI/CD pipelines (GitLab CI) to ensure environment parity and reliable deployments.
• Pragmatic Agile: Short iterations, continuous delivery, and honest retrospectives over rigid ceremony. I care about shipping working software every sprint, keeping the backlog actionable, and maintaining a feedback loop tight enough to catch problems before they compound.
• Observability by default: Structured logs, distributed tracing and alerting are part of the service design, not afterthoughts. If the system is not transparent, it cannot be trusted.