• Role: Data & Machine Learning Engineer.
• Focus: Designing resilient, scalable data architectures and deploying ML models.
• System Design: Passionate about distributed systems; avid reader of Designing Data-Intensive Applications and Software Architecture: The Hard Parts.
• Certifications: Databricks (Professional & Associate), AWS (ML Specialty), Google Cloud (Pro ML Engineer), Neo4j Professional.
• Education: MSc in Computer Science (108/110) from University of Milan.
• Achievements: Winner of the NASA Space Apps Challenge 2025 (Zurich local competition).
• Interests: Rock climbing, Board games, PC gaming, and Competitive Programming.

Category	Technologies
Languages
Cloud
Data Eng
ML & AI
DevOps

May 2024 – Present

• Enterprise-Scale Data Pipelines & Historical Data Optimization: Designed high-volume data ingestion pipelines (Databricks, Medallion Architecture) for Prada; optimized historical tracking with SCD Type 2, reducing query time by 80%.
• Optimized Data Orchestration: Migrated Airflow DAGs to GCP Composer 2.x, boosting scheduling efficiency by 10x for Becko.
• Sanctioned Shop Blocker: Optimized sanctions-screening lakeflow job for customer onboarding; significantly improved performance and reduced data size to a quarter.
• CI/CD: Established CI/CD pipelines in Azure DevOps and GitHub Actions to automate code testing with pytest and Databricks Asset Bundles.
• RAG-powered CloudFormation Template Generator: Developed a UI-driven tool to generate AWS CloudFormation templates from natural language. The engine retrieves AWS formatting documentation to guarantee a well-written YAML output.

• ETL Refactoring: Optimized IBM DataStage workflows and SQL for banking data warehouses.