Data Engineer

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Work Experience

Data Engineer - Associate @ PwC Italy (March 2024 - Present)

• Designed and developed ETL pipelines using Azure Data Factory and Databricks, optimizing data processing for large-scale datasets.
• Implemented CI/CD pipelines on Azure DevOps for automating workflows, reducing deployment time by 30%.
• Managing pull requests for DevOps repositories, overseeing deployments across Databricks environments.
• Migrated Databricks environments (dev, qa, prd) using Terraform, ensuring seamless deployment and management across environments.
• Built and optimized Power BI dashboards to monitor key business metrics and ensure data accuracy.
• Developed and maintained data flows on Microsoft SQL Server, ensuring integration between various sources.

Operations Specialist @ Daytrip (Feb 2023 - Dec 2023)

• Managed a portfolio of 9000+ drivers and handled 150+ daily support tickets during high-demand periods, showcasing strong problem-solving skills in a fast-paced, remote environment.
• Improved workflows and automated processes, resulting in a 90% increase in annual bookings while leading the Operations team.
• Standardized communication processes across a remote team, ensuring efficient operations and collaboration through online tools (Slack, Google Workspace).

Technical Skills

Coding & Tools: Python, SQL, Data Factory, Databricks, DevOps, CI/CD, Git, REST APIs, Power BI, Tableau
Databases: Microsoft SQL Server, PostgreSQL, MySQL
Cloud Platforms: Microsoft Azure, Google Cloud Platform
Other: Excel, Microsoft Office, Google Workspace, Slack, AI tools (ChatGPT, Gemini, GitHub Copilot, etc.)
Languages: Italian (native), English (fluent, C2 Cambridge Certificate)

Education

• Master in Data Analytics, start2impact University (Nov 2023 - Present)
• Batchelor’s Degree in Economics and Commerce, Università degli Studi di Napoli Federico II (Sep 2018 - Mar 2022)

Projects

Google Play Store Apps analysis

View the code on Github

In my second project, I simulated a business case in which a developer has to create a new app for the Android market, and wants to make sure they know the best possible conditions in order for it to be as highly rated as possible. To find what type of app would perform best, I analyzed the Google Play Store Apps Kaggle Dataset, using Python and its libraries Pandas, matplotlib and Seaborn. My analysis found what the most popular categories, genres and content ratings are for apps in the Google Play Store.

Renewable Energy SQL Project

View the presentation
View the code on Github

For my first project, I used PostgreSQL to analyze the Kaggle Dataset Renewable Energy World Wide : 1965~2022. This dataset on renewable energy contains data on renewable energy usage worldwide from 1965 to 2022. My analysis emphasizes how in the last ~15 years, countries have started making a bigger effort towards using a higher amount of renewable energy in their energy mix - however, this still hasn’t been enough to heal decades of abuse on our planet.