Call a Controller: Sarah Schubach, Dropbox

The 10-year Dropbox veteran shares tips for hiring the best people, using AI, and more

Welcome to our new series Call a Controller, where I hop on the phone to get some practical advice from finance operators at fast-moving companies.

In our first episode, I spoke with Dropbox VP and Controller Sarah Schubach, who’s seen the company grow from Series B to IPO and beyond.

Sarah shares plenty of useful insights from her 10+ years of experience growing Dropbox’s finance function, including:

• Why it’s critical to hire the right people early on

• Her top tip for preparing for audit (as an ex-auditor!)

• What her team has learned from using generative AI

Watch the full conversation to hear Sarah’s expert perspective.

Transcript:

What’s one thing you were glad you took care of early in your company’s growth?

Early on in our growth, we made sure that we hired really smart people and that we thought about how we could scale processes once we were public. We were in this state of perpetually 18 months away from an IPO and always trying to make sure that we were thinking about how we would tackle different problems once we were a public company.

I think that was super critical because obviously we didn’t do everything perfectly, but we were able to think into the future and make sure that we were putting the right processes and people in place so that once we were public, we wouldn’t be scrambling on the backend to deal with things like an audit.

I think that helped us out really well. If we had delayed that, it would’ve been a huge challenge to catch up once we went public. Because there are a lot of other challenges that you go through on the finance team once you are a public company.

What’s your top tip for preparing for an audit?

Make sure you have everything documented really well. And you document that in a few different ways. You want to know what your processes are at the time so that when you’re talking to your auditors, you’re not scrambling to try and figure out what you did six, nine, twelve months ago.

And also make sure that you document, in the actual work you’re doing, the review process that took place, how people were calculating things—very standard if you’re an ex-auditor, but it can be lost when you’re a small company, small team, growing really quickly.

I think in some ways we did that well early on in my company and in some ways we were challenged in that regard. So documenting processes was really critical.

What’s your biggest worry or challenge heading into 2024?

In the last couple of years, and I think this continues in 2024, it’s making sure that we are retaining our really strong performers on the team. The job market, especially in tech, is hot, and people are able to bounce from company to company. And when you spend a lot of time recruiting and hiring really good, smart, driven people, you want to make sure to keep it that way.

You don’t want people just doing kind of the same thing over and over, repetitive mindless tasks. So we’re thinking about how we can leverage some of the current technologies that have been emerging over the last few years to take away some of those repetitive tasks from our team. And make sure that we can put people on high value, really interesting projects that are going to keep them at the company even as we continue to scale.

We have been definitely trying to experiment with AI to try and reduce some of the time that we spend looking at a lot of data. We have a lot of transactions at our company and it can be really challenging to sift through some of that data. So we’ve been experimenting with some generative AI on things like accounting fluxes, both for revenue and operating expenses.

And it’s been good so far. There have been some challenges. You learn a lot about your data infrastructure underlying things like your journal entries or your subledgers, that can be either helpful or not very helpful when you’re trying to get some of those insights. And so we’ve also taken a step back and gone back to some of our underlying data infrastructure to say, if we want to use generative AI, we’re going to have to do X, Y, and Z. So let’s make sure that we set ourselves up over the next few months to be able to get those scalable processes in place.

So it’s been really interesting. There’s definitely a lot of potential, but it does uncover some things and there can be some challenges when you don’t have a perfect data infrastructure, which nobody does. ⊞