I build end-to-end data systems — from real-time ingestion and processing to analytics, visualization, and intelligent decision-making. My work combines data engineering, cloud-native architectures (AWS), and applied machine learning, with a focus on scalability, robustness, and real-world impact.

My work focuses on building scalable data systems that combine analytics, cloud-native architectures, and applied machine learning.
Exploring data, identifying patterns, and generating insights through visualization and analytical thinking.
Building scalable data flows for ingestion, transformation, and processing across different systems.
Designing cloud-native solutions focused on scalability, reliability, and efficient system design.
Applying machine learning to solve real-world problems with focus on performance and robustness.
Selected projects that showcase my work across data analytics, cloud architecture, and applied machine learning.

Pipeline fog-cloud orientado a eventos para la ingesta, persistencia, análisis y notificación de eventos EEG utilizando servicios de AWS.

Sistema de recomendación de películas basado en filtrado colaborativo, KNN, métricas de similitud y backend REST en C++.

Dashboard de analítica en tiempo real sobre datos de Codeforces: progresión de rating, distribución de problemas por tag/dificultad y métricas de rendimiento, consumidas directamente desde la API pública.