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.


What You'll Learn:

• How to escape the "dashboard chaos" trap by adopting a metrics-first architecture with a semantic layer, reducing confusion from hundreds of conflicting dashboards to a single source of truth across the organization

• Why replacing Tableau with Steep (a metrics-centric BI tool) unlocked self-service analytics for non-technical users, empowering teams to answer their own data questions without waiting months for custom dashboard builds

• The real-world cost optimization challenge of managing Snowflake expenses that scale 1:1 with ride volume—and why data leaders must constantly rethink architecture to control FinOps in high-growth environments

• How to architect for IoT at scale: processing billions of daily events from connected vehicles using micro-batch pipelines (5-minute intervals) while keeping real-time machine learning inference separate through cross-functional product teams

• The decision framework for choosing traditional ML vs. LLMs: use traditional methods for accuracy-critical workloads (supply-demand forecasting for vehicle positioning) and LLMs for pattern discovery where 100% precision isn't required (analyzing rider feedback)

•  How to build proactive customer support powered by data and AI: leverage sensor data and ride telemetry to detect poor user experiences and reach out before customers complain, rather than waiting for refund requests

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About the Guest(s)


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.

Quotes


"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

Resources 

Connect on LinkedIn:

• Magnus Dahlbäck - https://www.linkedin.com/in/magnusdahlback/
• Benjamin Wagner - https://www.linkedin.com/in/wagjamin/
  
  

Websites:

• Guest's Company: Voi Technologies Website (voi.com)
• Host's Company: Firebolt Website (firebolt.io)

Tools & Platforms:

• Snowflake – Data warehouse for analytics and machine learning workloads
• DBT (Data Build Tool) – Data transformation and modeling
• Apache Airflow – Workflow orchestration
• Steep – Metrics-first BI tool with semantic layer (Swedish startup)
• GCP Vertex AI – Machine learning platform for model training and deployment

The Data Engineering Show is brought to you by firebolt.io and handcrafted by our friends over at: fame.so

Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.

Check out our three most downloaded episodes:

• Zach Wilson on What Makes a Great Data Engineer
• Joe Reis and Matt Housley on The Fundamentals of Data Engineering
• Bill Inmon, The Godfather of Data Warehousing