Experience

The culture was something rare: a fully remote team that still felt genuinely connected, collaborative, and invested in each other. I had real ownership, made real decisions, and moved fast on things that mattered. My voice was heard and a different perspective was welcomed, not feared. The data team (and the entire Rinsed team) was truly extraordinary. We regularly shared knowledge through Lunch and Learns and Ninety meetings, which kept everyone aligned and learning together. This was my first start-up experience and it set the bar for what I want in every role after.

Rinsed introduced me to DBT and pushed me to use Python scripts, GitHub Actions, and AI tools like Claude to move faster and build better. I learned to programmatically update Metabase dashboards at scale, saving hours of manual work. I competed in a 24-hour hackathon with a team of four to reimagine the data analytics portion of our CRM using Replit, earning second place and a spot on the product roadmap. I also co-lead a full BI tool evaluation, coordinating demos and building a comparison framework for both internal and tenant-facing needs.

The impact came from a few directions. During tenant interviews, I flagged a churn risk with an enterprise customer, partnered with the CSM to dig in, and helped resolve it before it became a loss. I led the Stacksync integration connecting HubSpot and Zendesk for real-time two-way sync, owning it from kickoff through documentation and a Lunch and Learn. I built a product status mapping dashboard in Metabase and a DBT model to power it that gave leadership weekly visibility into billing gaps and cross-sell opportunities. I helped serve hundreds of tenants across multiple POS systems, which meant building and maintaining unified data models from sources that rarely aligned.

Rinsed was a place I was genuinely excited to show up for every day. In hindsight, I learned the importance of asking bigger questions earlier about a business’s trajectory. Being laid off unexpectedly reinforced the value of staying curious about the bigger picture, not just the work in front of me.

Entertainment data is genuinely fun. I loved tracking a guest's journey from the app to the bay, including wait times, food, and player experience, and working with Toptracer, which uses cameras (not RFID) to track every ball. I was proud to witness my reports being used at venues across the US.

Merging labor data from three non-standardized POS systems into a single model was painstaking work. It tied directly to payroll and timecards, so accuracy wasn't optional. Timestamps are tricky enough on their own, and trickier when each source delivers the data differently.

I owned SSAS cubes relied on by 100+ users daily and surfaced labor data that helped venues run more efficiently. I also aggregated player surveys to give leadership a clearer picture of the guest experience and where to improve.

A shifting org structure and a heavy reliance on contractors made ownership feel murky. There was a real tension between getting things done fast and doing them well. Pair that with a lack of leadership transparency, and building genuine buy-in for the right priorities was an uphill battle.

This role threw me into the deep end with high-pressure operations, live data issues and real urgency. I thrived on it. There's something satisfying about being the person who keeps the data flowing.

Working with tables that processed 500M+ new records daily forced me to write good code, not just code that worked. I picked up techniques like cardinality functions over COUNT DISTINCT. These small choices made a big difference at scale.

I built real-time Tableau dashboards paired with VictorOps and Slack alerts to monitor Databricks job health — turning reactive firefighting into proactive prevention.

Data teams and business teams pointing fingers at each other when a report breaks. I learned early that shared ownership and regular communication with end users is the only way to avoid this trap.

The pace was fast! We pivoted quickly, ran constant tests, and had real ownership over outcomes. This was the first role where I felt like a true owner, not just an executor.

This role introduced me to SQL and kicked off my data journey. I also completed a three-month internal Data Science Accelerator covering Scala, Python, GitHub, and Jupyter. I also got a lot of reps presenting, with my biggest audience topping 150 people.

I noticed we were showing prices based on assumed location often underpricing users who were actually in a higher tier. I designed and ran a test to capture location earlier in the flow, improving consistency and driving meaningful lift in conversion.

The hours were long, and at times analysis felt like analysis for its own sake. Often we were chasing numbers without always asking what they meant for the bigger picture.