Backend Engineer Kubernetes Infrastructure
Onehouse
Backend Engineer Kubernetes Infrastructure
Onehouse
Fully managed data lakehouse service platform
Design and develop Kubernetes Operators and Controllers using Go and controller-runtime.
Build Kubernetes-native automation for provisioning, upgrading, scaling, and managing distributed systems.
Develop Custom Resource Definitions (CRDs) and reconciliation logic to automate platform lifecycle management.
Build backend services that integrate with Kubernetes APIs and cloud infrastructure.
Improve the reliability, scalability, and observability of infrastructure software running on Kubernetes.
Collaborate with backend, data platform, and frontend engineers to deliver platform capabilities.
Design software that powers Spark and AI workloads on Kubernetes.
Participate in architecture discussions and contribute to long-term platform design.
Debug complex distributed systems running in Kubernetes production environments.
Maintain high engineering standards through code reviews, automated testing, and observability.
Nice to Have
This is not primarily a DevOps, Site Reliability Engineering (SRE), or Kubernetes cluster administration role.
While you'll work closely with cloud infrastructure and production systems, your primary focus will be designing and building Kubernetes-native software rather than day-to-day cluster operations.
Onehouse provides a fully managed data lakehouse on open storage that supports multiple table formats like Apache Hudi, Iceberg, and Delta Lake. It works by letting customers specify the data to organize and optimize, while the service automatically handles data management tasks such as clustering, compaction, masking, encryption, and change data capture, deployable in minutes. It differentiates itself with open-storage lakehouse principles, format flexibility, automated optimization, security features, and a pay-as-you-go model that lowers data-management costs. The goal is to make data management simpler and cheaper while enabling fast access for BI, real-time analytics, and AI/ML workloads.
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