Sr Software Engineer

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.

Telemetry data pipelines

• 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. 

Kubernetes, DevOps, and production ownership

• 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.

Full stack engineering and technical leadership

• 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.

Experience and education requirements

The job profile defines by scope, independence, and impact rather than years served. The minimums below are the screening bar for this requisition:

• Bachelor’s degree in computer science, software engineering, or a closely related – Required

• 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

A master’s degree may offset up to one year of the 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 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.

Desired qualifications

• 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.

AI skills

Artificial intelligence strategy is a critical skill at Experienced proficiency — it applies to how you build and to what you build.

• 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; familiarity with the Model Context Protocol is a plus.

• 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.

For more information

www.servicenow.com

Work Personas

We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.

Equal Opportunity Employer

ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.

Accommodations

We strive to create an accessible and inclusive experience for all candidates. 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 [email protected] for assistance.

Export Control Regulations

For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.

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