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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # BACKEND ENGINEER - **Company:** Svitla Systems Inc. - **Location:** United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Query Performance, Amazon Web Services, Acceptance Test-Driven Development, Automation of Tests, Microsoft Azure, Software as a Service, Cloud Computing, Cloud Engineering, Configuration Management, Code Review, Databases, System Configuration, Continuous Integration, Distributed Data Store, Distributed Systems, Github, Graph Database, Network Topologies, Networking Hardware, Python (Programming Language), PostgreSQL, Neo4j, Netconf, Network Administration, Open Source Technology, Performance Tuning, Query Optimization, Prometheus, Simple Network Management Protocols, Software Engineering, SQL Databases, Data Streaming, Web Services, Datadog, Data Logging, Google Cloud, Cloud Platform System, Real Time Systems, Data Ingestion, Istio, Grafana, Multi-Cloud, Backend, Juniper, Containerization, Kubernetes, Influxdb, Apache Kafka, Linkerd (Service Mesh), Restful APIs, Terraform, Data Pipelines, Dynatrace, Cisco, Docker - **Published:** September 17, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=6553e2255d5a6472 ## About the Role * Languages: Go (primary), Rust (for performance-critical components), Python (scripting and tooling). * Data & Streaming: Kafka or Kinesis, InfluxDB or TimescaleDB, PostgreSQL, and graph stores for topology. * Telemetry Protocols: gNMI, NETCONF, SNMP, and custom streaming collectors. * Infrastructure: Kubernetes, Docker, Terraform, and service mesh (Istio or Linkerd). * Cloud: Multi-cloud environments across AWS, GCP, and Azure; platform-neutral design is a first-class requirement. * Observability: OpenTelemetry, Prometheus, Grafana, and structured logging. * CI/CD: GitHub Actions or equivalent, with automated testing gates for all services., * 10+ years of backend software engineering experience, with a strong track record of shipping production services at scale. * Proven hands-on proficiency in Go and/or Rust, with experience building production systems using these languages. * Solid experience building and operating distributed data systems, including event streaming (Kafka, Kinesis, or equivalent), time-series databases, and high-throughput ingestion pipelines. * Strong fundamentals in distributed systems, including consistency models, failure modes, backpressure, idempotency, and the trade-offs between exactly-once and at-least-once processing. * Experience with cloud-native deployment, including containerization with Docker, Kubernetes orchestration, and operating services on at least one major cloud platform (AWS, GCP, or Azure). * Familiarity with observability tooling, including structured logging, metrics (Prometheus/Grafana or equivalent), and distributed tracing (OpenTelemetry or equivalent). * Comfortable writing SQL and working with both relational and time-series data models, with experience in schema design and query optimization. * Strong engineering practices, including test-driven or test-informed development, code review, CI/CD, and operational ownership, including participation in on-call rotations. Will be a plus * Experience with network telemetry protocols (gNMI, NETCONF, SNMP) or prior experience working at a network equipment vendor or network management platform company (e.g., Juniper, Cisco, or Nokia). * Hands-on experience with graph databases or graph data models for network topology (e.g., Neo4j, Amazon Neptune, or custom adjacency representations). * Familiarity with YANG data modeling or experience working with device configuration schemas in network management contexts. * Experience building multi-tenant SaaS backend services with strong data isolation and per-tenant resource management. * Contributions to open-source infrastructure, observability, or networking projects, or published technical writing on distributed systems topics. * Experience working in or adjacent to regulated, security-conscious environments (e.g., FedRAMP, NIST, or equivalent). ## Description The Backend Engineer will design, build, and operate high-performance backend services and distributed data systems at scale. The role focuses on Go and/or Rust development, high-throughput telemetry ingestion and real-time processing, distributed systems, cloud-native infrastructure, observability, and production reliability. The engineer will work closely with the Principal Architect and product team to translate monitoring requirements into robust backend solutions and will contribute to architecture, technical design, performance optimization, and engineering standards across the platform., * Design, implement, test, and operate production backend services in Go and/or Rust, writing clean, performant, and well-tested code as a primary daily responsibility. * Build and maintain high-throughput data ingestion pipelines that collect telemetry from large device fleets using protocols including gNMI, NETCONF, SNMP, and custom streaming transports. * Implement real-time processing engines, including aggregators, normalizers, and anomaly detectors, that transform raw device telemetry into actionable platform signals. * Develop and maintain RESTful and gRPC API services that expose platform data to internal consumers, dashboards, and external integrations. * Implement and evolve the platform's time-series storage layer using databases such as InfluxDB, TimescaleDB, or equivalent, with a focus on query performance, retention, and data fidelity. * Build event-streaming consumers and producers using Kafka, Kinesis, or equivalent, ensuring reliable at-least-once or exactly-once processing semantics as required. * Implement data partitioning, sharding, and archival strategies that support high event volumes while meeting latency and durability SLAs. * Work with graph data models for network topology representation, supporting device relationship queries and topology-aware alerting. * Own the reliability of the services you build by participating in on-call rotations, responding to incidents, writing post-mortems, and driving systemic fixes. * Instrument services with meaningful observability, including structured logging, distributed tracing, and metrics that enable fast diagnosis of production issues. * Contribute to and follow platform-wide engineering standards for testing (unit, integration, and load), CI/CD pipelines, and deployment practices. * Participate in capacity planning, performance profiling, and optimization of backend services under production load. * Package and deploy backend services as containerized workloads on Kubernetes, following platform patterns for resource management, autoscaling, and service mesh integration. * Write and maintain infrastructure as code for service deployments, configuration management, and environment parity across cloud providers (AWS, GCP, or Azure). * Contribute to multi-cloud and multi-region deployment patterns that ensure platform availability and meet data residency requirements. * Collaborate with the Principal Architect and product team to translate monitoring requirements into backend implementation plans and engineering specifications. * Participate actively in architecture and design reviews, providing implementation-level feedback and raising concerns grounded in production experience. * Conduct and participate in thorough code reviews, maintaining a high bar for correctness, performance, and maintainability. * Mentor junior and mid-level engineers through pairing, code reviews, and technical guidance. ## Related Videos - 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