Lead Software Engineer (API & Microservices development)

NetApp, Inc.•San Jose, CA

2d•$215,000 - $245,000•Onsite

About The Position

NetApp's Azure NetApp Files (ANF) team builds cloud storage services that power enterprise workloads in Microsoft Azure. We are looking for a senior software engineer focused on designing, developing, and owning backend microservices and REST APIs used to create, manage, update, and delete service objects across cloud-scale distributed systems. The ideal candidate has strong production software development experience in one or more of Go, C++, or C#, has built APIs and microservices through the full software development lifecycle, and is comfortable working with containers, Docker images, Kubernetes-based services, databases, and modern CI/CD practices.

Requirements

8+ years of software engineering experience building production software, backend services, distributed systems, or cloud-native platforms.
Hands-on development experience in at least one of the following languages: Go, C++, or C#.
Demonstrated experience developing REST APIs or service-to-service integrations as part of core product or platform work.
Experience building microservices end-to-end, including design, implementation, testing, deployment, and production support.
Experience with Docker and containerized service development, including building or maintaining Docker images.
Experience developing or modifying Kubernetes-based services or components; administration-only Kubernetes experience is not sufficient.
Experience using databases as part of application or API development, preferably PostgreSQL or MongoDB. Database administration alone is not the target experience.
Strong software quality practices, including automated testing, code reviews, CI/CD, observability, and secure development practices.
Ability to communicate design tradeoffs, implementation plans, and project status clearly to technical and cross-functional stakeholders.
Creative and analytical approach to solving complex engineering problems.

Nice To Haves

Experience building and operating cloud-native distributed systems in Azure, AWS, or GCP.
Experience with cloud storage services, infrastructure services, distributed systems, databases, data protection, or data management platforms.
Experience leading small to medium engineering projects or owning major service components from design through release.
Experience with Python for automation, tooling, validation frameworks, CI/CD integration, or operational automation.
Familiarity with React, TypeScript, event driven / message brokering architecture (rabbitmq, Kafka, etc).
Experience applying AI tools responsibly in software development workflows, such as code assistance, test generation, documentation, or productivity improvement.

Responsibilities

Design, develop, and own production-quality REST APIs and backend service components for ANF cloud services.
Build API functionality end-to-end, including design, implementation, unit and integration testing, deployment, monitoring, and production support.
Develop cloud-native microservices using containerized architectures, Docker, Kubernetes, and modern CI/CD pipelines.
Contribute to service object lifecycle workflows, including APIs used to create, manage, update, and delete resources.
Work across the full SDLC, translating customer, product, and platform requirements into scalable, secure, maintainable services.
Develop or modify Kubernetes-based service components; this role requires engineering depth beyond basic Kubernetes usage or administration.
Use database technologies such as PostgreSQL or MongoDB as part of application and API development, including schema-aware service integration and data access patterns.
Collaborate with architects, product management, partner teams, and peer engineers to deliver reliable cloud-service capabilities.
Participate in architecture reviews, design discussions, code reviews, and operational readiness reviews.
Troubleshoot production issues, perform root cause analysis, and implement long-term improvements for reliability, scalability, performance, and service quality.
Apply AI-assisted engineering practices appropriately to improve day-to-day development productivity while maintaining code quality, security, and ownership.