Benton Partners

Role Description As a Platform Engineer at Cube, you will be responsible for designing, building, and maintaining the core platform that powers Cube cloud product and supports Cube Core at scale. Your primary focus will be reliability, scalability, performance, and developer productivity across our infrastructure and internal platforms. You will work closely with Core and Platform engineers to ensure Cube runs efficiently in production environments and provides a seamless experience for both internal teams and customers. What you will do: - Designing and maintaining cloud-native infrastructure for a globally distributed analytics platform. - Building internal platform tooling to improve developer velocity, observability, and deployment safety. - Scaling multi-tenant systems that serve large volumes of analytical queries with strict latency requirements. - Improving reliability, fault tolerance, and disaster recovery for Cube cloud platform. - Operating Kubernetes-based environments and evolving our deployment and release pipelines. - Implementing monitoring, alerting, and incident response practices for a high-availability system. Qualifications - Strong experience with cloud infrastructure (AWS, GCP, or similar). - Hands-on experience with Kubernetes, container orchestration, and infrastructure-as-code (Terraform or equivalent). - Solid understanding of distributed systems, networking, and Linux internals. - Experience building and operating production systems with high availability and performance requirements. - Proficiency in at least one programming language used for platform development (Go, Rust, Python, or similar). - Good communication skills and ability to work effectively in a remote, async environment. - Fluent English. Requirements - Experience operating data-intensive or analytics-heavy platforms. - Familiarity with CI/CD systems and release automation. - Experience with observability stacks (Prometheus, Grafana, OpenTelemetry, etc.). - Background in security best practices for cloud platforms. - Contributions to open-source infrastructure or platform projects. Benefits We’re a fully remote company based in San Francisco. You can work from anywhere and join our highly collaborative team. Cube does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits. If you need reasonable accommodations during the interview process, please discuss this with the recruiter and we'll gladly work with you.

As a Platform Engineer at Cube, you will be responsible for designing, building, and maintaining the core platform that powers Cube cloud product and supports Cube Core at scale. Your primary focus will be reliability, scalability, performance, and developer productivity across our infrastructure and internal platforms. Designing and maintaining cloud-native infrastructure for a globally distributed analytics platform. Building internal platform tooling to improve developer velocity, observability, and deployment safety. Scaling multi-tenant systems that serve large volumes of analytical queries with strict latency requirements. Improving reliability, fault tolerance, and disaster recovery for Cube cloud platform. Operating Kubernetes-based environments and evolving our deployment and release pipelines. Implementing monitoring, alerting, and incident response practices for a high-availability system.

As a Full-Stack Engineer at Cube, you will work across the frontend and backend of Cube Cloud platform and developer-facing tools. Your goal will be to deliver high-quality product features that make complex analytics infrastructure intuitive, powerful, and accessible to developers and data teams. Building and evolving the Cube Cloud platform web application and developer-facing interfaces. Designing and implementing backend APIs that power configuration, management, and analytics workflows. Translating complex analytics concepts into clear and usable UI/UX. Improving performance, reliability, and scalability of end-user features. Working with large datasets and real-time analytics results in frontend applications. Collaborating with Core engineers to integrate new Cube capabilities into the product.