Julio Jeronymo

Led an end-to-end migration of legacy Power BI business logic into a modern Looker Core environment. Automated the translation of complex measures and indicators using Vertex AI and Claude Code to keep logic parity. Improved dashboard performance and scalability through the new analytical setup.

Converted high-volume MySQL routines to a BigQuery and Dataproc-based architecture and refactored query logic using AI-assisted coding workflows. Built production-grade orchestration with Apache Airflow and optimized BigQuery schemas for large multi-petabyte datasets. Implemented serverless infrastructure with Terraform and Cloud Run, and delivered modular transformations with dbt plus visualization layers in Looker. Established GitLab CI/CD pipelines to speed up deployments while maintaining result parity.

Implemented a Medallion architecture (Bronze/Silver/Gold) in Databricks for data preparation and governance. Orchestrated API data ingestion and workflows using Azure Data Factory and Azure connections. Used PySpark to support cross-functional data integration and automate data preparation to accelerate stakeholder reporting and analysis.

Managed large-scale data ingestion pipelines for Hadoop (HDFS) and Teradata for a major retail client. Automated ETL workflows and performed operational log analysis using scripting and RStudio Server Pro on Red Hat Linux. Focused on stability and reliability for critical business data extractors.