Описание

About the role We are hiring a Sr. software engineer for the Telemetry Data Platform team, which carries product usage telemetry & performance telemetry for the entire ServiceNow platform on a stack built with Kubernetes, Kafka, and ClickHouse. We are distributed across Israel, India, and the Americas. This is a full stack role with its center of gravity firmly on telemetry development, data pipelines, Kubernetes and DevOps work, supporting Billions of records per day. You will be equally comfortable designing a distributed system and owning it in production. If you like following a system from the event that fires to the chart that renders — and you want the pager for it — you will feel at home. You will lead design and delivery of complex, multi-service features, own technical decisions within a domain, and are fully self-directed — escalating only genuine ambiguities and driving decisions to closure. Of the ten critical skills at this level, incident response management is the only one set at Expert; the rest are Experienced.

Responsibilities

Design, build, and own backend services for distributed data streaming and processing that handle high-volume, high-cardinality event data with predictable latency and no silent data loss
Build and maintain Kafka-based streaming pipelines and the pipeline components that feed ClickHouse
Own data modeling and query performance in ClickHouse — partitioning, sort keys, materialized views, retention, and the cost curve that comes with all of it
Partner with product and platform teams to shape requirements for telemetry ingestion and processing, then drive the solutions to production
Deploy, scale, and operate services in production Kubernetes environments, including Helm-based deployments and CI/CD pipelines that make releases repeatable and safe to roll back
Own observability for what you build — meaningful metrics, useful logs, real tracing, and alerts that fire on customer impact rather than on noise
Debug and resolve production incidents independently, participate in on-call, run root-cause analysis, and operate against defined service level objectives
Own the full development lifecycle for your work, and build the backend services and APIs that expose telemetry to internal consumers, product surfaces, and AI agents — including API contracts, data models, and schema migrations
Contribute to the analytics front end — dashboards, funnels, and exploration tools — with an eye on performance against large result sets, and improve the web and mobile capture SDKs
Lead the design of complex, multi-service features across team boundaries, drive decisions to closure, and write the design docs and postmortems that outlive the conversation
Raise the bar through code review, test strategy, and automation coverage, and mentor engineers on the team

Requirements

Bachelor’s degree in computer science, software engineering, or a closely related
6+ years’ experience as backend software engineer
4+ years’ experience in designing and operating distributed systems
3+ years’ experience with Kubernetes in production, including Helm and CI/CD
3+ years’ experience with Kafka or equivalent streaming and data pipelines
Experience with on-call and production support
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 an 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
Enough front-end capability to build and debug a data-heavy interface: JavaScript or TypeScript with React or Angular
Artificial intelligence strategy is a critical skill at Experienced proficiency
Confident use of AI coding assistants such as GitHub Copilot or Cursor, paired with the critical eye to review AI-generated code as rigorously as any other — catching logic errors, security anti-patterns, and missed edge cases rather than treating model output as production-ready
AI-assisted debugging and trace analysis, and a clear understanding of the data privacy rules governing what goes into a prompt
Integrating LLM APIs into production features — and knowing when a model is the wrong tool for the job
Working knowledge of RAG pipelines, embedding stores, and agent frameworks
Exposing telemetry to AI agents through tool interfaces agents can use safely: predictable schemas, bounded results, clear error semantics, sane cost controls
Designing safeguards for non-deterministic components — retry logic, circuit breakers, human-in-the-loop checkpoints — and hardening against latency spikes and hallucinated output reaching a customer
Instrumenting AI features in production and judging their quality from telemetry and evaluation rather than impressions

Nice to have

Production experience with ClickHouse or another OLAP or 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
Familiarity with the Model Context Protocol

We offer

Employee Type: Regular
Work Persona: Flexible

Hiring process

If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact ••• for assistance