Rodrigo Carranza

I’m a Junior Data Engineer transitioning from 3+ years in automation and AI projects, with a strong focus on building reliable data pipelines in production environments.

I designed and optimized an image processing pipeline using Python and Computer Vision (YOLO), reducing bottle recognition time by 75% through improved data cleaning and processing. That hands-on engineering mindset carries over to my approach to ingestion, transformation, and storage.

I’ve built end-to-end ETL pipelines with Python, SQL, PostgreSQL, and PySpark—using Medallion Architecture (Bronze → Silver → Gold) and star schema modeling. I also orchestrate workflows with Apache Airflow, including DAG dependency management and scalable Parquet-based outputs for analytical querying.

I’m expanding my skills with Databricks and cloud platforms, while completing the IBM Data Engineering Professional Certificate (including ETL and Data Pipelines with Shell, Airflow and Kafka). I enjoy shipping modular, config-driven pipelines—such as my INEI education ETL—and continuously improving data lifecycle reliability.