About the job
Location
As a Principal Data Engineer, you will play a vital role within our Engineering teams across EMEA. This position is fully remote for candidates based in the UK, Ireland, Estonia, the Netherlands, Sweden, and Israel. We also welcome contractors from Eastern Europe and Portugal.
About Us
DoiT is a leading global technology company that collaborates with cloud-centric organizations to harness the power of the cloud for business growth and innovation. We merge data, technology, and human expertise to ensure our clients are well-architected and scalable from planning to production.
Through DoiT Cloud Intelligence, the exclusive solution that integrates advanced technology with human insight, we assist our clients in overcoming complex multicloud challenges and enhancing operational efficiency.
With decades of multicloud expertise, we specialize in Kubernetes, GenAI, CloudOps, and more. As an award-winning strategic partner of AWS, Google Cloud, and Microsoft Azure, we proudly serve over 4,000 customers globally.
Introducing DoiT's PerfectScale Platform
DoiT presents PerfectScale, an innovative Kubernetes optimization and management solution designed to empower DevOps, SRE, and Platform Engineering teams to enhance cloud performance while effectively managing costs. By combining cutting-edge AI technology with expert human knowledge, we help organizations achieve optimal Kubernetes efficiency.
This solution offers a seamless onboarding experience, an intuitive user interface, and a robust autonomous optimization engine that ensures Kubernetes environments operate efficiently with minimal human oversight.
Your Role
As a Principal Data Engineer, you will serve as both a hands-on contributor and a primary architectural leader. You will be responsible for designing and developing large-scale backend services and high-throughput data pipelines while also influencing the long-term technical trajectory of PerfectScale’s platform. This position merges profound technical ownership with significant contributions to critical code, infrastructure, and performance-sensitive workloads.

