Portfolio

A modern, privacy-aware data pipeline that ingests nutrition and fitness data, applies AI recommendations, and powers real-time dashboards, Built to showcase scalable, production-grade data architecture.

• Ingests fitness and nutritional data from multiple personal tracking sources.
• Integrates comprehensive tests for data quality, schema enforcement, and expected ranges.
• Uses AI-generated synthetic data to safely validate pipeline logic without exposing real data. 
• Automatically classifies and redacts personally identifiable information (PII).
• Applies dbt contracts with column-level metadata and validation.
• Includes consent and safety logic for ethical data modeling.
• Supports real-time dashboards with trend analysis and alerting.
• Built using modern tools: dbt, BigQuery, Airflow, Cloud Functions, Streamlit.

This platform is part of an ongoing project to demonstrate high-quality, AI-integrated data architecture. A full case study and clean demo repo are coming soon.

Want early access or a walk-through? Let’s talk.