Juan Pérez Llorente

Collaborated on designing and developing scalable data pipelines in Databricks, implementing the Medallion architecture to streamline data ingestion and transformation Developed and deployed a Databricks job using Change Data Capture (CDC) to update tables daily from an external JDBC source, ensuring real-time, up-to-date client insights. Engineered an ETL pipeline using Python to automate data cleaning, transformation, and loading into Metabase, reducing manual effort by 90% and enhancing data accuracy and consistency. Enhanced SQL queries to incorporate a benchmarking feature, allowing users to compare their performance against competitors and market trends, with the ability to display insights across multiple centers simultaneously. Acted as a bridge between engineering and product teams, ensuring alignment and resulting in a smooth and timely launch of the latest improving user adoption by 15%.

Designed and developed a comprehensive data pipeline to extract, transform, and load financial metrics into a PowerBI dashboard. This enabled real-time monitoring and analysis of clinic performance, enhancing decision-making capabilities. Collaborated with the senior management team to optimize data models and refine forecasting algorithms, resulting in improved data accuracy and reliability for financial analyses and strategic planning.

Developed a data loading and reporting automation system to store transformed data in MySQL and execute SQL queries via Python, generating structured financial reports. Designed a batch data extraction and transformation process using Pythion to ingest processed billing data from Power BI, ensuring data accuracy and consistency. Created a Google Data Studio dashboard to visualize customer churn trends, providing actionable insights for business decision-making.