I find myself wearing the expert hat, not because I earned it, but because everyone who knows better is gone.

Now, let me tell you about the state of our data. At the time, we didn’t know. That was the issue. It was a black box. Our data model was opaque with logic scattered all across the data stack. As we pick around the edges a picture starts to form, a dense, thorny briar patch, each thicket a tangled mess of information. That's how I see it—unruly, interlacing, and chaotic. In this session, you will learn firsthand how we leveraged the modern data stack to lead our team out of the briar patch.

Use Cases for this session include:

- Data Engineers looking to better understand data modeling beyond star schemas, maybe even just getting started with DBT.
- Mid to Senior Analysts who need to better understand how the modern data stack and data modeling (data mesh primarily) empower analysts to build better insights at scale
- Analytics engineers who need help to "sell" analysts and decision-makers on the promise of thoughtful data architecture and data modeling.

**Capabilities:** I will showcase how we used Streamlit to streamline processes at Purple to address data quality concerns with our marketing data. It will lean pretty heavily on dbt and Snowflake. From a data perspective, it will skew pretty heavily towards marketing data needs. The transition we made in Snowflake was from development in one environment (production schema) to a three-tier environment (dev / QA / Prod) as well as the introduction of CICD into our process.

The modality is a story (not a lecture), to leverage the human element of experience with the intent to motivate and inspire. In this session, I am going to tell you the story of Serge Sookram, an analytics engineer with an appetite for anything that will kill him, fast food, fast cars, & fast women. Learn firsthand how he leveraged the modern data stack to lead his team out of the briar patch.