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ServiceNow is hiring a Staff Software Engineer (IC4) for the Telemetry Data Platform team, which handles product usage telemetry for the entire ServiceNow platform. The team operates a distributed infrastructure spanning Israel, India, and the Americas, built on Kubernetes, Kafka, and ClickHouse.
This is a full-stack role with primary focus on infrastructure. You will spend roughly two-thirds of your time on telemetry infrastructure, data pipelines, Kubernetes, and DevOps work, with the remainder on services and product surfaces. You will design and own distributed systems end-to-end, from event ingestion through production operation.
Key responsibilities include:
**Telemetry Infrastructure & Data Pipelines:**
- Design, build, and own backend services for distributed data streaming and processing handling high-volume, high-cardinality event data with predictable latency and no silent data loss
- Build and maintain Kafka-based streaming pipelines and components feeding ClickHouse
- Own data modeling and query performance in ClickHouse, including partitioning, sort keys, materialized views, retention, and cost optimization
- Partner with product and platform teams to shape telemetry requirements and drive solutions to production
**Kubernetes, DevOps & Production Ownership:**
- Deploy, scale, and operate services in production Kubernetes environments using Helm-based deployments and CI/CD pipelines
- Own observability for your work: meaningful metrics, useful logs, real tracing, and customer-impact-driven alerts
- Debug and resolve production incidents independently, participate in on-call rotations, run root-cause analysis, and operate against defined SLOs
**Full-Stack Engineering & Technical Leadership:**
- Own the full development lifecycle, building backend services and APIs exposing telemetry to internal consumers, product surfaces, and AI agents
- Contribute to analytics frontends—dashboards, funnels, exploration tools—with performance optimization for large result sets
- Lead design of complex multi-service features across team boundaries, drive decisions to closure, and author design docs and postmortems
- Raise the bar through code review, test strategy, automation coverage, and mentoring
You will be self-directed, escalating only genuine ambiguities. At this level, incident response management is set at Expert proficiency; other critical skills are at Experienced level.
**Requirements:**
Minimum experience (screening bar for this requisition):
- 8+ years of backend software engineering experience
- 4+ years designing and operating distributed systems
- 3+ years Kubernetes in production, including Helm and CI/CD
- 3+ years Kafka or equivalent streaming and data pipelines
- 3+ years on-call and production ownership
- Bachelor's degree in computer science, software engineering, or closely related technical field
Note: A master's degree may offset up to one year of experience minimum. Equivalent practical experience is considered where technical depth is clearly demonstrable.
Required qualifications:
- Strong backend development experience in Java, Python, Go, or equivalent, used in production at scale
- Hands-on experience building distributed systems for data streaming, processing, and storage
- Production experience deploying and managing services on Kubernetes, including Helm and CI/CD pipelines
- Working knowledge of Kafka or equivalent messaging and streaming system
- Solid SQL skills and hands-on experience with a columnar or analytical data store, including query optimization and physical data modeling
- Demonstrated depth in incident response and customer escalations
- Sufficient front-end capability to build and debug data-heavy interfaces: JavaScript or TypeScript with React or Angular
Desired qualifications:
- Production experience with ClickHouse or another OLAP/columnar store (sharding, replication, materialized views, cost tuning)
- Exposure to observability tooling: OpenTelemetry, metrics and logging stacks, alerting platforms
- Experience building product analytics or telemetry platforms, or with stream processing frameworks such as Flink or Spark
- Client-side instrumentation: web or mobile SDK development, event capture, session and consent handling
AI skills (Experienced proficiency required):
- Confident use of AI coding assistants (GitHub Copilot, Cursor) with critical review of AI-generated code for logic errors, security anti-patterns, and missed edge cases
- AI-assisted debugging and trace analysis with clear understanding of data privacy rules for prompts
- Integrating LLM APIs into production features; knowing when a model is the wrong tool; working knowledge of RAG pipelines, embedding stores, and agent frameworks; familiarity with Model Context Protocol is a plus
- Exposing telemetry to AI agents through safe tool interfaces: predictable schemas, bounded results, clear error semantics, cost controls
- Designing safeguards for non-deterministic components: retry logic, circuit breakers, human-in-the-loop checkpoints; hardening against latency spikes and hallucinated output
- Instrumenting AI features in production and judging quality from telemetry and evaluation
