Staff Software Engineer - Data Platform - Kubernetes - Distributed Systems - Federal

Job description

Position Location

This is a Flexible (Hybrid) position. Flexible positions require 2 days per week in a ServiceNow office location. We have offices in several locations, including San Francisco, CA; Pleasanton, CA; Santa Clara, CA; San Diego, CA; and Kirkland, WA Please Note: This position will include supporting our US Regulated Markets. “This position requires passing a ServiceNow background screening, USFedPASS (US Federal Personnel Authorization Screening Standards).

This includes a credit check, criminal/misdemeanor check and taking a drug test. Any employment is contingent upon passing the screening. Due to Federal requirements, only US citizens, US naturalized citizens or US Permanent Residents, holding a green card, will be considered.

About the team

We are seeking a Staff Software Engineer (IC4 Level) to join our Data Platform Engineering organization. You are a strong hands-on contributor who will own well-scoped components and projects within the team and deliver them end-to-end.

What you get to do in this role

You will design, build, and operate components and services within the cloud-native platform, taking features from design through to production. You will lead well-scoped projects within your team, turning requirements and rough ideas into solid technical designs and delivering them. You will collaborate with senior, staff, and principal engineers to align your work with the platform’s architecture and standards. You will contribute to the reliability, scalability, and operability of what you build — writing tests and instrumentation, improving runbooks, and participating in on-call.

You will spend most of your time hands-on building — operators, controllers, automation, and platform services that other teams depend on. You will help onboard and mentor junior engineers through code review, design feedback, and pairing.

Qualifications

To be successful in this role you have: Experience leveraging or critically thinking about how to integrate AI into engineering work — whether using AI-powered coding and operational tooling, automating workflows, or reasoning about how AI changes the way software and infrastructure are built. 8+ years of software development experience with a Bachelor's degree; OR 5+ years with a Master's degree; OR 3+ years with a PhD; OR equivalent work experience. 5+ years of hands-on experience building, deploying and operating workloads on Kubernetes.

Working experience with at least one major hyperscaler (AWS, Azure, GCP), including its core compute, networking, and IAM primitives. A track record of delivering complex features or services as a key contributor. Solid working knowledge of containers, CI/CD and GitOps-based delivery, and infrastructure-as-code. Strong programming skills in Go (or another systems language with a willingness to work primarily in Go).

It also helps if you have: Experience building Kubernetes controllers or working with the operator pattern. Familiarity with container networking (CNI) or service mesh. Experience with workload identity, mTLS, or secrets management. Experience with observability — metrics, tracing, and dashboards. Experience with managed Kubernetes (EKS/AKS/GKE) and infrastructure-as-code tools such as Terraform. For positions in this location, we offer a base pay of $149,800 - $262,200 , plus equity (when applicable), variable/incentive compensation and benefits.

Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs.

Compensation is based on the geographic location in which the role is located and is subject to change based on work location.