Scaling a 700-person Platform Org at Datadog with Ian Nowland

About Ian Nowland, former SVP of Engineering at Datadog:

Ian Nowland has been in the industry for 24 years. In 8 years at AWS, he was the lead engineer on the launch of EMR, and then the initiator and lead manager of the EC2 Nitro project. After 2 years managing the Compute platform at a fintech company, he moved to Datadog, where he ended up leading the 700 person Platform Engineering organization. He is just finishing co-authoring a book on Platform Engineering, and ideating on a stealth-mode startup.

Ian on LinkedIn and Twitter‍

Chapters‍

00:00 Introduction and Background
06:47 Balancing the Inner Loop and Outer Loop of Developer Experience
28:14 The Future of Platform Engineering and AI in Debugging

Summary‍

In this episode of the Hangar DX Podcast, Ian Nowland, former SVP of Engineering at Datadog, discusses the philosophy behind having a platform engineering team and the challenges of scaling an organization. He emphasizes the importance of culture in scaling and the need to balance the inner loop and outer loop of developer experience. Ian also shares his thoughts on KPIs, the use of metrics in decision-making, and the role of AI in improving developer experience. He concludes by discussing his current projects, including writing a book on platform engineering and exploring the potential of AI in debugging.‍

Keywords‍

Developer experience, platform engineering, scaling, culture, KPIs, metrics, AI, debugging.‍

Takeaways

• Platform engineering teams play a crucial role in companies like Datadog, where the platform itself is a product. The philosophy behind having a platform engineering team is to provide a unified user experience and ensure consistency across different products.
• Scaling an organization requires not only technical solutions but also a strong culture. Building trust, relationships, and understanding the priorities of different stakeholders are essential in managing a rapidly growing engineering team.
• Metrics can be useful in prioritizing and making decisions, but they should be used with caution. It's important to focus on a handful of metrics that align with the most important problems and to balance quantitative data with qualitative feedback.
• The inner loop of developer experience, which includes coding, building, and deploying, is crucial for productivity. Optimizing the inner loop and minimizing context switching can significantly improve developer efficiency.
• AI has the potential to simplify complexity in developer experience, such as debugging and code understanding. However, there are limitations to the current state of AI, and it's important to be skeptical and realistic about its capabilities.
• Ian Nowland is currently working on writing a book on platform engineering and exploring the potential of AI in improving debugging processes.