Diego Alberto Castillo

Built high-throughput telemetry ingestion services in Go to handle low-latency, high-volume event streams. Led development of Python-based analytical pipelines for processing telemetry data at scale. Implemented enterprise-grade integration services in Java to connect telemetry platforms with downstream systems and partners. Designed PQL-based queries to correlate logs, metrics, traces, deployments, and configuration changes across distributed systems. Created deterministic telemetry entities using PQL to model system state and detect risky changes before incidents. Led BYOC deployments on AWS and GCP using Kubernetes, and built embedding pipelines for low-latency similarity search.

Developed C# (ASP.NET Core) services for core business domains requiring strong transactional consistency. Implemented Node.js (Express.js) services for lightweight APIs and integration-focused workloads. Designed GraphQL gateways to unify access across internal microservices. Developed REST APIs to support third-party integrations and partner systems. Built React.js dashboards for internal analytics and operational tooling. Developed Next.js applications for customer-facing products requiring fast initial load times. Implemented Vue.js (Nuxt.js) frontends for modular admin panels and rapid feature iteration. Leveraged RabbitMQ and Kafka to enable asynchronous processing and real-time updates. Containerized applications using Docker and orchestrated services with Kubernetes. Automated CI/CD pipelines using Jenkins, reducing deployment time by 40%. Integrated automated testing with xUnit, Mocha, Jest, and Cypress to reduce production defects by 30%. Collaborated in Agile/Scrum teams, pa

Built and operated enterprise SaaS platforms with emphasis on observability, telemetry analysis, and reliability. Implemented Go-based real-time ingestion; Python for telemetry analytics; designed PQL-style workflows to reconstruct system behavior. Implemented Kafka-based pipelines, Go for throughput and Python for transformation. Supported customer production environments with live debugging and incident response; produced telemetry-backed operational insights for leadership. Deployed Kubernetes workloads on AWS and GCP with automated CI/CD.