A secure data engineering pipeline implemented in Python using the Titanic dataset. It demonstrates best practices for handling sensitive information (PII) by applying AES-128 encryption (Fernet) before storage in a SQLite database, ensuring data privacy while maintaining analytical capabilities through secure decryption methods.

A Kubernetes and Vault-based end-to-end secure pipeline with a sample Flask application.

Fully automated GCP pipeline: Python → GCS → Cloud Data Fusion (salary masking + SHA-256 password hashing) → BigQuery → Tableau | Daily Airflow orchestration