As a Staff Engineer on Datadog's Compute – Disruption and Workload Placement team, you'll help define how our Kubernetes fleet scales to meet the demands of rapidly growing AI and cloud-native workloads. You'll work on the systems that ensure engineering teams have the right compute capacity, in the right region, at the right time across AWS, Google Cloud, and Azure. This is a highly technical, high-impact role where you'll shape the future of capacity orchestration, influence platform architecture, and solve infrastructure challenges that directly support Datadog's continued growth.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You'll Do:
- Lead the technical direction of capacity management and workload placement for Datadog's Kubernetes platform spanning 100,000+ virtual machines across multiple cloud providers.




- Design and build systems that optimize how engineering workloads are scheduled and deployed across regions while balancing capacity constraints, reliability, and performance.
- Partner across infrastructure teams to evolve multi-region and multi-cloud capacity orchestration as Datadog continues to scale.
- Develop production software in Go to improve Kubernetes platform capabilities, automation, and operational efficiency.
- Use data and capacity signals to influence infrastructure decisions, forecast growth, and improve workload placement strategies.

Who You Are:
- You have significant experience designing and operating large-scale Kubernetes-based infrastructure or platform systems.
- You are an experienced software engineer with strong programming skills, ideally in Go or a comparable systems programming language.
- You have hands-on experience with at least one major cloud provider (AWS, Google Cloud, or Azure) and understand distributed cloud

📌 Staff Engineer, Compute (Madrid)
🏢 Datadog
📍 Madrid