Software, data, and AI engineer based in Padova, Italy. I build backend services, data platforms, analytics workflows, and machine learning systems that connect reliable engineering with practical decision support.
My background combines production software development, cloud data engineering, applied analytics, and NLP research. I hold a Bachelor's degree in Electrical Engineering and a Master's degree in ICT from the University of Padova, where my thesis focused on BERT-based models for emotion and slur prediction. I also completed an informatics and enterprise analytics mobility program at the University of Mannheim.
Across professional and academic work, I have built REST APIs, PostgreSQL-backed services, billing and IoT platform modules, ETL/ELT pipelines, BI-ready datasets, forecasting systems, and NLP/ML workflows. I like work where clean architecture, data quality, automation, and measurable outcomes matter.
| Area | What I Work On |
|---|---|
| Backend & software engineering | TypeScript/Node.js services, REST APIs, Strapi 5, PostgreSQL models, access control, Stripe/webhook flows, scheduler jobs, Dockerized delivery |
| Data engineering | Python/SQL pipelines, Airflow orchestration, PySpark transformations, dbt models, BigQuery warehouses, Terraform-managed cloud infrastructure |
| Analytics & BI | KPI datasets, dashboard-ready models, reporting automation, data validation, forecasting, stakeholder-oriented analysis |
| ML & AI | NLP classification, BERT/Transformer embeddings, recommendation systems, time-series forecasting, feature engineering, evaluation workflows |
| Category | Tools & Technologies |
|---|---|
| Languages | Python, SQL, TypeScript, JavaScript, R, Bash, C++ |
| Backend | Node.js, Strapi 5, REST APIs, OpenAPI, PostgreSQL, Knex, Stripe SDK, webhooks |
| Data & cloud | Airflow, Spark/PySpark, dbt, BigQuery, GCP, AWS S3/Lambda, Terraform, Docker |
| ML & analytics | Scikit-learn, TensorFlow, Hugging Face, Pandas, NumPy, SciPy, StatsModels, Prophet |
| Visualization | Power BI, Looker Studio, Tableau, Matplotlib, Seaborn, Plotly, Streamlit |
| Engineering workflow | Git/GitHub, Linux, CI/CD concepts, structured logging, validation, documentation |
| Project | Focus | Highlights |
|---|---|---|
| AI Weather Predictor | ML application | End-to-end weather forecasting workflow with preprocessing, model logic, cloud-oriented design, and a Streamlit interface for human-readable 7-day forecasts. |
| Bookwise AI | Recommendation system | Semantic book recommendation app using Sentence-BERT, enriched book metadata, user-facing search, feedback logging, and deployment-ready infrastructure concepts. |
| Energy Forecast Pipeline | Data engineering & forecasting | Cloud-native batch pipeline with Terraform, Airflow, PySpark, BigQuery, dbt, Prophet forecasting, and Power BI outputs for energy-demand analysis. |
| Retail Data Pipeline | Data platform | Dockerized Airflow and BigQuery pipeline for retail ingestion, transformation, validation, and dashboard-ready analytics. |
| Data Mining | NLP & deep learning | Reddit popularity classification workflow with text preprocessing, embeddings, class-balancing techniques, model evaluation, and reproducible analysis. |
| Google Play Data Analysis | Analytics | Cleaning, exploration, and visualization of app-market data to study ratings, categories, monetization, and product trends. |
| Road Sign Detection | Computer vision | Object-detection workflow for road-sign recognition using computer vision techniques and model evaluation. |
| Customer Segmentation | Applied ML | Customer behavior segmentation using clustering methods and exploratory analysis. |
- Building production-grade backend and data systems with strong validation, observability, and maintainable service boundaries.
- Designing cloud data workflows that turn raw operational data into reliable analytics and ML-ready datasets.
- Applying NLP, forecasting, and recommendation techniques to practical product and business problems.
- Strengthening MLOps and deployment practices across containerized, cloud-oriented applications.
I am always interested in thoughtful software, data, and AI work: backend platforms, data engineering, analytics automation, ML systems, and research-driven product ideas.