Staff Software Engineer, Observability

Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience.

Product Mindset: Demonstrated ability to work backwards from customer needs —understanding user needs, prioritizing features, measuring success, and iterating based on feedback. Experience building internal platforms or tools with strong adoption

Distributed Systems Expertise: 7+ years of experience designing and operating large-scale distributed systems with deep understanding of consistency, availability, scalability, and failure modes

Data Engineering Skills: Strong background in building data pipelines, working with time-series databases, columnar storage, stream processing (Kafka, Flink, etc.), and data modeling at scale

Observability Domain Knowledge: Hands-on experience with modern observability tools and practices including metrics, logging, tracing, and profiling. Familiarity with OpenTelemetry, Prometheus, Grafana, or similar technologies

Programming Proficiency: Expert-level coding skills in languages like Java, Python, Go, or Scala with ability to write production-quality code

Define and execute the observability roadmap, treating it as a product. Understand engineering team needs and translate them into technical solutions with measurable impact.

Architect, build, and scale distributed observability infrastructure (metrics, logs, traces) to handle massive volumes across Pinterest's distributed systems.

Build high-performance data pipelines and storage for real-time and historical telemetry analysis at Pinterest scale.

Champion Best Practices: Establish observability standards and patterns across the organization, making it easy for teams to instrument their services and gain actionable insights

Technical Leadership: Mentor engineers, lead architectural reviews, and influence technical decisions across teams to improve overall system reliability and performance

Cross-functional Collaboration: Partner with SRE, Infrastructure, Product Engineering, and other teams to understand pain points and deliver solutions that improve developer productivity and system reliability