Senior Software Engineer

At Nimbus Data Systems, we’re reinventing DevOps to help the world’s greatest companies innovate – and we want you along for the ride. This is a special place with a unique combination of brilliance, spirit and just all-around great people. Here, if you’re willing to do more, your career can take off. And since software plays a central role in everyone’s lives, you’ll be part of an important mission. Thousands of customers, including the majority of the Fortune 100, trust Nimbus Data Systems to manage, accelerate, and secure their software delivery from code to production – a concept we call “liquid software.” Wouldn't it be amazing if you could join us on our journey? We are looking for a hands-on developer to join an innovative and talented R&D team. To join us, you will need to deliver high-quality code using the latest cloud frameworks and technologies. As a Back-End Engineer at Nimbus Data Systems you will... Be responsible for all development aspects, from design to production, of a cloud-native product Write high-quality, testable, and efficient code in Go/Python/Java Initiate and promote new ideas for the continuous improvement of product functionality Work closely with team members and product colleagues Lead feature development end-to-end (E2E), from initial architectural design to successful production deployment. Write high-quality, clean, efficient, and maintainable code while adhering to best practices and coding standards Stay up-to-date with industry trends and emerging technologies related to DevOps and the software development lifecycle Contribute to architectural discussions and design sessions to promote scalability and maintainability To be a Back-End Engineer at Nimbus Data Systems you need... 6+ years of Go/Python/Java programming experience Familiarity with database technologies Solid architectural understanding of web applications and APIs Experience with large-scale production environments Experience with concurrency – a big advantage Exposure to modern DevOps tools like Docker, Kubernetes, Helm, Shell Scripting, CI/CD – an advantage Proven, hands-on AI engineering experience – required in one or more of the following: Building full production features using AI-native coding tools such as Cursor or Claude Code. Adapting the SDLC to leverage MCPs and AI Skills to measurably improve team productivity. Integrating agentic AI capabilities into production systems and user-facing features.