Principal Software Engineer & Architect, Cyber Defense Engineering

ServiceNow•Santa Clara, CA

2d•$221,200 - $387,100

About The Position

The Principal Software Engineer & Architect will own the technical direction of the data and platform systems behind detection, investigation, hunting, response, and AI-driven defense for ServiceNow's Cyber Defense Engineering organization. The role is responsible for the architecture of a multi-system security data platform, spanning ingestion through access, and for decisions about data placement, business value, performance, retention, cost, and reducing duplication across systems. The position will define shared schemas, data models, and data contracts so telemetry is consistent, trustworthy, and joinable for people, automation, and AI. It will build the data foundation for applied AI in security operations, including data and feature pipelines, model evaluation, retrieval, and governed data access for AI agents investigating and responding to threats. The architecture must incorporate access control, data protection, auditability, and compliance. The Principal Engineer will lead technical design across teams, resolve tradeoffs on shared platforms, maintain coherent architecture as the organization grows, and drive major decisions to closure through design and decision documents. The role also sets engineering standards for source control, CI/CD, testing, and observability; mentors engineers through design and code review; and connects platform investments to measurable security outcomes such as detection coverage, time to detect, investigate, and mitigate, and data quality and freshness.

Requirements

Bachelor's degree in computer science, software engineering, or a similar field.
10+ years of software engineering experience, including 3+ years in a principal, staff, or architect role.
Proven experience designing and operating large-scale cloud data platforms in production, including data modeling, data warehouse design, and high-volume streaming and batch telemetry pipelines across multiple systems.
Hands-on ML/AI experience, including shipping ML- or LLM-based systems to production, and working knowledge of how data quality, freshness, and lineage limit model performance.
Proficiency in SQL and at least one general-purpose language: Python, Go, or Java.
A record of setting technical direction across teams without formal authority.
Clear, concise writing and a habit of documenting decisions and their rationale.

Nice To Haves

Experience applying ML and graph-based analysis to security problems such as anomaly detection, entity correlation, incident investigations, or attack path analysis.
Experience in security operations, such as SIEM platforms, security datalakes, detection engineering, or incident response.
Knowledge of secure data access patterns such as RBAC/ABAC, masking, and policy-as-code.
Advanced degree in machine learning, AI, or a related field.

Responsibilities

Own the architecture of the multi-system security data platform, from ingestion to access, including data supporting detection, investigation, hunting, and risk analytics.
Make decisions about data placement by balancing business value, performance, retention, and cost, and remove duplication across systems.
Define shared schemas, data models, and data contracts to make telemetry consistent, trustworthy, and joinable for people, automation, and AI.
Build data and feature pipelines, model evaluation, retrieval, and governed data access supporting AI agents in security operations.
Build access control, data protection, auditability, and compliance into the platform architecture.
Lead technical design across teams, resolve shared-platform tradeoffs, and maintain coherent architecture as the organization grows.
Drive major technical decisions to closure through design and decision documents.
Set engineering standards for source control, CI/CD, testing, and observability.
Mentor engineers through design and code review.
Tie platform investments to measurable outcomes, including detection coverage, time to detect, investigate, and mitigate, and data quality and freshness.