Principal Software Engineer - Capella Control Plane

Couchbase Capella is a fully managed Database‑as‑a‑Service (DBaaS) platform that powers mission‑critical applications. The Control Plane Platform team builds and operates the foundational systems that deliver reliability, scalability, and seamless experiences across cloud environments.

Location: San Jose, California

We are seeking a Principal Software Engineer to lead the architecture of Capella’s Control Plane – the platform that orchestrates SaaS interactions with major cloud providers (AWS, GCP, Azure). In this role, you will mentor engineers, deliver core platform capabilities, and combine hands‑on development with deep expertise in distributed systems and cloud architecture leadership, while contributing to the architecture and integration of state‑of‑the‑art AI/ML knowledge and process execution into the Capella Control Plane.

• System Design & Architecture
  :
  Define and evolve the control plane architecture for scalability, reliability, performance, and security.
• Hands‑on Development
  :
  Develop and deliver production‑quality Golang code for critical Capella platform services. Participate in guiding the team on Golang coding standards and practices.
• Cloud Infrastructure & Networking
  :
  Architect and optimize multi‑cloud deployments, networking models, VPC/VNet peering, load balancing, and secure connectivity for Capella.
• Cloud‑First SaaS Expertise
  :
  Build and scale multi‑tenant control plane services that orchestrate Couchbase clusters across cloud providers.
• Technical Standards
  :
  Establish and enforce coding standards with strong code reviews, CI/CD practices, and design patterns.
• Observability & Reliability
  :
  Define and implement monitoring, logging, tracing, and alerting standards for operational excellence.
• High‑impact Features
  :
  Lead design and delivery of core platform capabilities such as provisioning, lifecycle management, and security controls.
• Problem Solving
  :
  Tackle complex distributed systems challenges and resolve scaling, networking, and performance bottlenecks.
• Cross‑functional Collaboration
  :
  Work with product, SRE, quality engineering, and operations teams to align technical direction with business goals.
• Multi‑geo Team Collaboration
  :
  Partner effectively with global engineering teams across geographies, driving alignment and execution.
• Mentorship & Leadership
  :
  Guide junior and mid‑level engineers, conduct reviews, and set a high bar for engineering culture. Perform code reviews in your area of influence.
• Innovation
  :
  Research and evaluate cloud‑native technologies, frameworks, and architectures for Capella’s evolution. Lead the technical strategy, design, and integration of AI/ML services and foundational models into the control plane to enhance platform capabilities and execution efficiency.
• Support & Troubleshooting (Tier
  3):
  Lead root cause analysis and deliver long‑term solutions for critical production issues.
• Define and optimize processes for AI model deployment, lifecycle management, and observability within the multi‑cloud architecture.

• Expertise in Golang with proven experience building distributed, cloud‑native systems.
• Strong knowledge of system design, high availability, fault tolerance, and performance tuning.
• Hands‑on experience with cloud infrastructure (AWS, GCP, Azure), including networking (VPCs, routing, load balancing, firewalls, private connectivity).
• Experience with Kubernetes, container orchestration, and multi‑cloud deployments.
• Experience designing and building platform and microservice components for control plane of SaaS platforms.
• Experience mentoring engineers and influencing the engineering culture.
• Hands‑on problem solver who can balance strategic architecture with execution.
• Demonstrated experience with AI/ML platform architecture and integrating advanced AI/ML services (e.g., vector databases, foundational models) into large‑scale, production‑ready SaaS platforms.
• Strong understanding of data pipelines and model serving infrastructure necessary for state‑of‑the‑art AI process execution.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
• 12+ years of professional…