Backend and Fullstack Developer with professional experience building production-ready APIs, services, and web applications.
Currently transitioning towards Data Engineering, working on data pipelines, workflow orchestration, and distributed systems.

I design and build systems with an end-to-end mindset, combining backend engineering with data-driven architectures.
My work spans from data ingestion and processing to storage, exposure through APIs, and final consumption by analytics or business tools.

My professional background is primarily in backend and fullstack development, where I have designed and implemented scalable services, APIs, and system integrations.

I am particularly interested in environments where:

• Data is treated as a product, with clear ownership and quality standards
• Engineering decisions consider scalability, maintainability, and operational impact
• Backend services and data pipelines work together as part of the same ecosystem

• Design and development of backend services and REST APIs
• Fullstack application development
• Relational and NoSQL data modeling
• Design and implementation of ETL / ELT data pipelines
• Workflow orchestration with Apache Airflow
• Event streaming and messaging with Apache Kafka
• Containerized environments using Docker
• Cloud-based deployments on AWS

• Python – scripting, data processing, backend services
• TypeScript / JavaScript – backend & frontend development

• RESTful API design
• Backend services architecture
• Authentication & integrations

• PostgreSQL – relational data modeling
• MongoDB – NoSQL document-based storage
• Data transformation & feature engineering

• Apache Airflow – workflow orchestration
• Apache Kafka – event streaming & messaging
• ETL / ELT pipelines

• Docker – containerized environments
• AWS – cloud-based deployments

Descripción: Diseño e Implementación de un Pipeline para Vulnerabilidad Hídrica y Social.
Tecnologías: Python, PySpark, Airflow, AWS
Repositorio: GitHub link

Descripción: Diseño y entrenamiento de modelos de ML para predicción de fraude.
Tecnologías: Python, FastAPI, Scikit-learn, Hugging Face.
Repositorio: GitHub link

💼 LinkedIn: https://www.linkedin.com/in/carlos-a-mignone/
🐙 GitHub: https://github.com/carlos-a-mignone

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