Giovanni Pacman
Back to the Greenhouse 🌵
Modern Data Lifecycle & Governance Practices
CI/CD DBT GitHub Snowflake

Summary

This page outlines a collection of data engineering practices developed and applied across multiple projects. These include managing the full data lifecycle, implementing DBT modeling standards, and enforcing governance policies. The consistent use of tools like DBT and Snowflake enabled reliable, scalable, and secure data platforms tailored to different business needs.

Context & Challenge 🧩

In multiple data engineering projects, there was a recurring need for scalable pipelines that met rigorous standards for data quality and governance. The challenge was to manage the full data lifecycle while ensuring accuracy, transparency, and secure access—across diverse business domains and cloud environments.

My Role & Contributions 🧑‍💻

I took a leading role in designing and implementing end-to-end solutions covering data modeling, testing, and governance:

  • Managed full data lifecycles with snapshotting, star schema design, and modular DBT transformations.
  • Documented all tables and columns with business and technical context.
  • Enforced quality with built-in and custom DBT tests.
  • Built CI/CD workflows in GitHub Actions to test and validate every code update.
  • Developed dynamic data masking in Snowflake via DBT macros for role-based access.
  • Tracked data lineage using DBT artifacts, ensuring end-to-end visibility.
  • Applied access controls, tagging, and logging to support governance and auditability.

    • Outcomes & Learnings 🚀

    This work led to tangible improvements in how data platforms were built, maintained, and governed:

  • Delivered secure, well-documented, and transparent data pipelines.
  • Accelerated development cycles with fewer bugs and stronger validation.
  • Empowered compliance teams with clear lineage and auditability.
  • Enabled dynamic, role-based access control without compromising usability.
  • Promoted consistent, test-driven practices across teams and environments.

    • Overall, it reinforced my belief in treating data engineering as a disciplined software practice—balancing speed, quality, and governance.