> Markdown version of [/jobs/ext/3847448-senior-staff-software-architect](https://www.wearedevelopers.com/jobs/ext/3847448-senior-staff-software-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Staff Software Architect - **Company:** Ionq, Inc. - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Computing Platforms, Microsoft Azure, Software as a Service, Cloud Computing, Cloud Engineering, Databases, Information Engineering, Distributed Systems, Monitoring of Systems, Network Topologies, Networking Hardware, Netconf, Network Administration, Open Source Technology, Simple Network Management Protocols, Software Engineering, Data Streaming, Autoscaling, Istio, Multi-Cloud, Juniper, Containerization, Kubernetes, Data Pipelines, Cisco, Microservices - **Published:** October 20, 2026 - **Apply:** https://www.juju.com/job/23_D98037CE84D6DA7A6589FDA1FA9F4F5C ## About the Role Requirements 12+ years of experience in software or platform architecture, including at least 5 years leading architecture for large-scale, data-intensive distributed systems. Substantial experience building and operating device monitoring systems at scale, including telemetry ingestion, fleet management, and real-time alerting. Proven hands-on production coding ability, with Go and/or Rust strongly preferred. Deep expertise in data modeling, schema design, time-series storage, event streaming, and graph data models for network topology. Solid understanding of cloud-native application design, containerization, Kubernetes orchestration, microservices, and cloud infrastructure across AWS, GCP, or Azure. Preferred: proficiency in both Go and Rust with production service delivery experience in at least one. Preferred: background with network management platforms or network equipment vendors such as Juniper, Cisco, or Nokia, and familiarity with gNMI, NETCONF, or SNMP. Preferred: experience designing multi-tenant SaaS platforms with data isolation, schema evolution, and customer-tier scalability. Preferred: contributions to open-source infrastructure or observability projects, published technical writing on distributed systems or data engineering, and familiarity with high-throughput data collection techniques. ## Description Architect cloud-native ingestion and processing pipelines for telemetry from hundreds of thousands of devices using streaming platforms and time-series storage. Define and own a unified YANG-based data model covering device configuration, operational telemetry, and network topology relationships. Write and review production-quality Go and/or Rust code for collectors, aggregators, and real-time processing engines. Define data partitioning, sharding, and retention strategies for high-volume device event streams while meeting latency and durability requirements. Design cloud-native deployment patterns using Kubernetes, service mesh, and autoscaling across multi-cloud environments. Evaluate and adopt time-series databases, graph stores, and stream processors for scalable, maintainable operations. Partner with Product and Engineering to translate monitoring requirements into platform capabilities, roadmap milestones, and engineering specifications. Mentor senior engineers through architecture reviews, coding standards, and hands-on technical guidance. Responsibilities Architect cloud-native ingestion and processing pipelines for telemetry from hundreds of thousands of devices using streaming platforms and time-series storage. Define and own a unified YANG-based data model covering device configuration, operational telemetry, and network topology relationships. Write and review production-quality Go and/or Rust code for collectors, aggregators, and real-time processing engines. Define data partitioning, sharding, and retention strategies for high-volume device event streams while meeting latency and durability requirements. Design cloud-native deployment patterns using Kubernetes, service mesh, and autoscaling across multi-cloud environments. Evaluate and adopt time-series databases, graph stores, and stream processors for scalable, maintainable operations. Partner with Product and Engineering to translate monitoring requirements into platform capabilities, roadmap milestones, and engineering specifications. Mentor senior engineers through architecture reviews, coding standards, and hands-on technical guidance.