What does data work actually look like inside a massive, real-time platform like Lyft?

In this episode of Mavens of Data, we'll sit down with Desmond Chan, a manager on Lyft’s Driver team, to talk candidly about the realities of building data products in a large organization, where spatial and temporal data are core to the business.

We'll dig into the challenges of working with time-based and location-based data, how teams are structured and scoped at scale, and what it really means to build product sense in a data team.

Desmond will share how Lyft’s driver org thinks about projects, how analysts and data scientists collaborate on the same team, who owns problem scoping, and how teams stay aligned while still moving independently. We'll also explore the driver journey and how data teams translate that journey into measurable, actionable work.

If you’ve ever wondered what working in a large tech organization actually feels like, or how data teams operate when the product lives in the real world, this episode is for you!

What You'll Learn:

• What makes spatial and temporal data uniquely hard at scale

• How Lyft’s Driver team is structured and what they’re focused on

• How Desmond thinks about building product sense on his team