Bruins in Tech: Nick Handel, Co-Founder of Transform Data

Nick Handel (c/o 2012) is the founder and CEO of Transform Data, an early stage startup in San Francisco backed by Redpoint and Index.

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Tell me about yourself and Transform

I studied math and economics at UCLA and knew I loved math, but I didn’t know what I wanted to do with it, so I started off at Blackrock. I worked on a global macro-fund, learned a ton of macroeconomics, and applied some of the math I learned in college. Ultimately, I wanted something a little faster paced, so I joined Airbnb as a data scientist in 2014 where I met both of my co-founders. Airbnb was really committed to data at the time and there was a big push to improve data tooling. A bunch of popular open source projects came out of that time, like Airflow and Superset.

One of the tools I worked on was a metrics repository: a place where all the data scientists defined all the business metrics and data metrics they consumed, for all the things they were doing, like analysis, reporting, etc. It was hugely impactful within Airbnb and dramatically changed my workflow. After I left in 2018, I joined Branch International as the head of data and was reflecting on the tooling I missed most, which was really that metrics repository tool. That was the thing that I was really passionate about and inspired by.

So I left Branch in 2019 and started walking around SoMa, talking to lots of data scientists, VCs, founders, basically anyone that would talk to me about data tooling. It became very clear that this was an unsolved problem, and the tooling at Airbnb was years ahead of the current best-in-class solution. I bounced around ideas with James, my former manager at Airbnb and Paul, an engineer I worked closely with, and we started Transform at the end of that year!

What’s the most common use case for Transform?

Every business that collects data uses that data to do analysis. Typically, you do the same analysis over and over again, and you calculate the same data point over and over again, eventually formalizing it into a metric. Transform helps you track all of those metrics, making sure there are clear, consistent definitions, for all of the data applications including BI, anomaly detection, reporting, and experimentation. Regardless of how you’re going to use the metric, Transform helps you stay on the same page with everyone else when it comes to calculating and understanding it.

What was it like transitioning from finance at Blackrock to tech?

I think people in tech assume that data science outside of tech is dissimilar to data science in tech and it’s really wrong. It’s all using some combination of intuition, math, and stats to take data, understand it, and make decisions from it. We used different tools at Blackrock, like MATLAB and Excel, compared to Airbnb where I had Hadoop, Hive, and Python. Despite the tooling being different, the core understanding of taking those tools and making use of data didn’t really change.

From a career change perspective, it was pretty challenging to convince everyone that what I had been doing for the last 2.5 years of my career was actually valuable and translated into me being a good candidate. That became less of an issue after I joined and found my place at Airbnb.

How do you think the role of data inside companies is changing? Where do you see that going?

There are an increasing number of data applications, so its becoming easier to push data from product all the way to the applications for that data. It’s also getting easier and easier for non-engineers to contribute to data infrastructure, like data analysts, this rising role of analytics engineer, and what was historically data scientists. All of these people are now involved in understanding the output and making sure that it’s clean, accurate, and useful for whatever application they want to pursue. At the same time, there’s a bunch of new tooling around data quality, anomaly detection, finance, and reporting. So I think we’ll see a lot more data consumption and a larger set of people who participate in the definition and management of that data, which is important because there are more people who can influence how that data should be used to improve decision-making at companies.

Thinking back to your time at UCLA, how was your experience there, and how did the UCLA community help you on your trajectory?

I was at UCLA for two years and two quarters, so I had a slightly shorter time there and ended up taking a lot of classes really quickly. Because there were so many different things going on at UCLA, I got exposure to a very wide variety of different ideas that I could pursue in my life. I realized that I really like endurance and athletic pursuits, so I joined the club triathlon team. I like working hard towards a goal, which for me was to get a degree a little bit faster. The thing I really learned and enjoyed most was discovering that this balance of athletic pursuits and working hard towards an academic goal could happen simultaneously.

What are some ways the UCLA community can help you?

The biggest thing is come work at Transform. The company is growing really quickly, and we’re hiring the smartest people we know. We’ve had a lot of success growing the team, and are always looking for people who really care about data and making it easier for people to access and use data.

Besides that, I find it extremely valuable when people who care about ideas want to talk about those ideas, so I’d just really like to talk to anyone who’s building in or thinking about this space. I hope having a more well connected UCLA network will help us act on and chat about these things more often.