Engineering with Impact:
Data Platform at TRM

What led you to TRM?

After graduating in computer science from the University of Pennsylvania, I worked on autonomous vehicle data infrastructure at Lyft. My team was responsible for ingesting petabytes of data and building large scale distributed systems, APIs and data products.

I decided to join TRM after reading a report about the digital assets data and infrastructure landscape, which described a growing demand for actionable blockchain intelligence.

I decided to join TRM after reading a report about the digital assets data and infrastructure landscape, which described a growing demand for actionable blockchain intelligence. With my background in data and machine learning infrastructure, I was interested in how that can be applied to blockchain forensics. I was also looking for a fast-paced, high growth startup.

Tell us a bit about your experience at TRM.

It is exciting to be at a company at this stage of growth. The challenge and sheer velocity of the work, along with the scope of responsibility, constantly pushes me out of my comfort zone. We ship very fast. As a result, every season of my time at TRM has looked extremely different. It took time to adjust to constantly being in-flux, but I am certainly never bored!

What has it been like working on the TRM Forensics tool?

I enjoy working on TRM Forensics because I get to solve interesting data challenges while maintaining a focus on the customer. Forensics allows users to analyze cryptocurrency addresses and entities and trace digital asset flows among them. You can plot flows between crypto addresses and entities on a single graph and drill into an individual address or entity to understand its asset and risk portfolios, transactional history, and direct and indirect counterparty exposure, among other insights.

One of my favorite projects was building the data pipelines and interfaces that support a Forensics feature that visualizes transactional relationships between graph elements that users have not yet plotted on their graph. It was rewarding to learn that this feature enables investigators to find new links in large complex graphs.

You said you like the 80/20 rule, tell us more!

I’m continually improving my ability to apply the 80/20 rule to my work. That’s the principle that 80% of the impact often comes from 20% of the input. In my work, this looks like narrowing scope and focusing on the engineering work that solves the most pressing user needs.

That means individual engineers have a wide scope of responsibilities with large impact.

Honing this skill is critical to our team’s success, because although TRM is scaling quickly, we still have a lean engineering team. That means individual engineers have a wide scope of responsibilities with large impact. The environment is challenging but forgiving. At TRM, it’s expected that you will fail sometimes: the point is to learn from your mistakes, fail differently the next time, and never stop learning!