Staff Software Engineer: Platform R&D
SolarWinds4 hours ago
Responsibilities
- Design and implement distributed backend systems supporting high-throughput, low-latency data and service workloads.
- Lead architecture and technical direction for services handling data ingestion, transformation, storage, and serving.
- Build and improve real-time and near-real-time data pipelines for large-scale platform use cases.
- Drive reliability, scalability, operability, observability, automation, fault tolerance, recovery, rollout safety, and capacity planning.
- Partner across engineering teams on interfaces, schema evolution, and safe service integration patterns.
- Improve performance across storage, compute, messaging, caching, and API layers.
- Contribute to platform modernization and backend architecture strategy and execution.
- Mentor senior and mid-level engineers through design reviews, code reviews, and operational leadership.
- Participate in incident response and post-incident improvement work.
Requirements
- 8+ years of software engineering experience, including significant experience building and operating production backend systems at scale.
- Strong distributed-systems expertise, including trade-offs involving consistency, availability, durability, and performance.
- Experience building data-intensive applications or platform services with event-driven or streaming architectures.
- Strong programming skills in one or more modern languages such as Rust, Go, Java, or Python.
- Experience with cloud-native infrastructure, containerized environments, APIs, service-to-service communication, and scalable data storage systems.
- Strong debugging and performance-tuning skills across complex, multi-service systems.
- Ability to lead technically across team boundaries and influence architecture.
- Strong written and verbal communication skills for explaining technical trade-offs.
- Preferred qualifications include experience with observability, telemetry, stream processing, message brokers, distributed databases, analytical data stores, Kubernetes, cloud platform operations, schema evolution, metadata management, data modeling, and cross-functional technical initiatives.