Principal Data Platform Software Engineer - Distributed Systems - Kubernetes

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started.

Join us to put AI to work for people.

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For positions in this location, we offer a base pay of $240,100 - $420,200 , 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.

Responsibilities

As a Principal Engineer, you will help to set the technical direction for our cloud-native platform across multiple engineering teams and organizations. You will be part of a team defining the architecture and standards for how Kubernetes, distributed systems, and hyperscaler infrastructure are built and operated at scale.; You will assist in solving the hardest, most ambiguous technical problems in the platform domain — multi-cloud topology, control-plane design, workload isolation, identity and trust fabric, and reliability at the scale of hundreds of clusters and dozens of product workloads.; You will work with a team of principal engineers and engineering fellows, and other senior technical leaders to help drive consistent architectural decisions and the adoption of best practices across the entire platform ecosystem.; You will assist in identifying and mitigating the biggest technical risks in initiatives with C-suite visibility.; You will help mentor staff and senior engineers and shape the next generation of the organization’s technical leadership.

Requirements

Experience leveraging or critically thinking about how to integrate AI into engineering and platform work — whether using AI-powered tooling, automating operational workflows, building agentic systems for fleet visibility and operations, or reasoning about AI’s impact on how infrastructure is built and run.; 15+ years of experience with a Bachelor degree or 12+ years with a Masters degree, designing, building, and operating large-scale distributed systems in production, with deep expertise running Kubernetes at scale (multi-cluster, multi-region, multi-tenant).; Hands-on, authoritative experience across one or more major hyperscalers (AWS, Azure, GCP), including their managed Kubernetes offerings (EKS, AKS, GKE) and the networking, IAM, and capacity tradeoffs that come with each.; A proven track record of providing technical leadership across multiple engineering teams, influencing architecture and direction without relying on positional authority.; Deep expertise in the core building blocks of a modern platform: the operator/controller pattern, infrastructure-as-code and control planes (e.g., Crossplane), GitOps-based delivery, container networking (CNI), and service mesh.; Strong programming skills in Go and fluency across the cloud-native ecosystem.; Experience designing identity and trust fabrics for distributed systems — workload identity, mTLS, and standards such as SPIFFE/SPIRE.; Experience with multi-tenant workload isolation and runtime security (e.g., Kata Containers, sandboxed runtimes).; Experience building and operating platforms for regulated or federal markets (FedRAMP, air-gapped or self-hosted distribution, OCI bundling).; Experience with observability at scale — metrics, tracing, and SLO-driven operations across a large fleet.; A platform-as-product mindset: treating internal engineering teams as customers and the platform as a product with a roadmap, contracts, and a delivery pipeline.