I am a backend-focused software engineer with 5+ years of experience building scalable, event-driven systems in banking and enterprise environments.
My core expertise is in the .NET and C# ecosystem, where I have worked on regulatory reporting platforms, internal API automation, and reusable NuGet libraries that improved consistency across applications.
I have also contributed as a full stack developer, working with React, Angular, TypeScript, Node.js, NestJS, TypeORM, and SQL-based databases to deliver maintainable products and better user experiences.
Throughout my career, I have focused on modernizing legacy systems, improving performance, reducing operational errors, and supporting cross-functional teams with reliable engineering practices.
I enjoy working on backend architecture, data-heavy systems, and integrations that require strong attention to reliability, scalability, and code quality.
I am open to remote-first opportunities and continue improving my English to collaborate effectively in international teams.
Designed and maintained regulatory reporting systems for mandatory Central Bank of Argentina reports. Authored internal NuGet libraries, built internal API integrations, and supported AI tooling adoption for technical analysis and development workflow optimization.
Led backend redesign for an internal platform, optimized database queries and ORM configurations, and drove a full data migration initiative with scripts, rollback coordination, and post-migration validation.
Refactored database query structures and indexing strategies, integrated Google Maps API for geographic selection and visualization, and built automated test suites to reduce regressions and production incidents.
Contributed to migration from AngularJS to Angular 10, developed responsive UI components using Material Design, and resolved frontend performance bottlenecks to improve load times.
Bachelor of Science, Computer Engineering
Final project focused on an augmented reality application for large-scale demonstrative events. Academic project included a machine learning model for sentiment analysis on mobile platforms.
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