Manager, Cloud Infrastructure Engineering
New
C
CamundaSaaS Platform
Fully remote and global, Working fully remote across time zonesFull-TimeManager
SalaryUnited States: $172,600.00 to $278,300.00; United Kingdom: £108,400.00 to £178,300.00; Singapore: S$214,400.00 to S$321,500.00. The Annual Total Target Cash (base salary + 100% variable target, where applicable).
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Job Details
- Experience
- 3 - 5 years experience leading engineers
- Required Skills
- KubernetesPeople ManagementSaaS
Requirements
- 3 - 5 years experience leading engineers who build and run production infrastructure for a SaaS product.
- Strong technical background in cloud infrastructure engineering, platform engineering, SRE, or a related domain.
- Hands-on Kubernetes experience in production, including scaling, reliability, and cluster lifecycle.
- Experience working across more than one major cloud provider and understanding the trade-offs of running multi-cloud.
- Track record of working with senior engineers and stakeholders across teams to deliver major infrastructure improvements while raising reliability, scalability and keeping a focus on security.
- Strong people leadership: coaching, managing performance, setting priorities, and building an environment where engineers can do their best work.
- Clear thinking about where AI fits in infrastructure engineering work, with judgment about what's useful, repeatable, and responsible.
- Ability and/or willingness to use Camunda's product.
Responsibilities
- Lead and grow the team that runs Camunda's SaaS cloud infrastructure, with a focus on execution and strong engineering culture.
- Guide the design, delivery, and evolution of Kubernetes-based, multi-cloud infrastructure, with reliability, scalability, and security as the baseline.
- Work with product engineering, security, support, and other engineering leaders to make sure the platform lets teams ship safely and efficiently.
- Keep raising the bar on infrastructure automation, developer tooling, and platform guardrails so the service scales without adding friction.
- Define how the team uses AI as a repeatable part of infrastructure work: ops analysis, automation, incident response, documentation.
- Balance day-to-day operational stability with longer-term investments in multi-cloud expansion and new infrastructure capabilities.
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