In this episode of The Data Engineering Show, host Benjamin sits down with Magnus Dahlbäck, Senior Director of Data and Platform at Voi, to explore how a rapidly scaling European e-scooter company transformed its data infrastructure, adopted a metrics-first approach to analytics, and is now leveraging AI to solve real-time operational challenges across 150 cities and 150,000 vehicles.

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Magnus Dahlbäck is Senior Director of Data and Platform at Voi, a leading European micro-mobility company, where he oversees the data analytics team, platform infrastructure, and AI initiatives. With over four years at Voi, Magnus has scaled the data organization from three people to a comprehensive team of platform engineers, data analysts, and data scientists while architecting a modern data stack centered on metrics-first analytics and semantic layers. In this episode, Magnus shares insights on building scalable data platforms for IoT-heavy, real-world products, including strategies for managing billions of daily events, implementing self-service analytics, and balancing traditional machine learning with large language models. His work at Voi—where the data platform powers both internal analytics and customer-facing product features—demonstrates how thoughtful data architecture drives measurable business impact, making this conversation essential for data leaders navigating AI integration and data democratization.

"There are hundreds of dashboards, and I'm looking for some data, some metrics, and there are 10 dashboards that contain that, and they all show different numbers." - Magnus

"Metrics is a very natural way of interacting with data rather than dashboards that are named something randomly." - Magnus

"We're basically throwing man hours on slicing and dicing data, trying to find patterns, anomalies that we often miss, right, because it just takes too much time." - Magnus

"The way we work with data hasn't really changed that much in the last ten, twenty years to be completely fair, but now we're seeing new technologies, new approaches to it." - Magnus

"It comes down to the use case. What's the accuracy we need?" - Magnus

"We can see from the sensor data, from the IoT, from other data points during your ride if it was a good or bad experience, so why don't we reach out to you?" - Magnus

"Building software around physical objects is really cool when you're a techie guy like me, working at a company where it's a combination of software, B to C, hardware, IoT." - Magnus

"The biggest dataset that we process is IoT data—billions of events every day, basically, that we process." - Magnus

"We have cross functional teams where all the product teams have everything from back end to front end to data people, designers, and so on." - Magnus

"Metrics is kind of the business language that we use—we talk about rides, average ride charge, active vehicles—so metrics is a very natural way of interacting with data." - Magnus