Director of Data & Storage Reliability & Platform

ServiceNow; Santa Clara (CA); On-site; USD 260,000 - 380,000; Full time

Benefits offered by this job

Generous family leave

Matched donations

Annual learning stipends

Flexible PTO

Competitive retirement plan

Paid volunteer time

Job summary

ServiceNow seeks a Director to lead the Data & Storage Reliability Engineering organization, improving reliability, observability, performance, scalability, and customer experience across database, storage, and platform infrastructure.

The role builds and scales engineering teams focused on reliability, observability, diagnostics, automation, and migration readiness, while driving systemic improvements and the adoption of AI-powered tooling.

Qualifications

• 15+ years of software/ platform reliability experience in large-scale SaaS.
• Strong product mindset with roadmaps, priorities, and measurable outcomes.
• Experience leading cross-functional engineering teams and driving data-driven improvements.

Responsibilities

• Lead Data & Storage Reliability Engineering org across database, storage, and platform infrastructure.
• Build, develop, and scale high-performing reliability/observability teams.
• Partner with Product, Cloud, Architecture, and Ops to embed reliability in SDLC.
• Establish KPIs, governance, and operating models for platform reliability.
• Drive AI-powered tooling to improve detection, insights, and productivity.

Skills

Reliability engineering

Observability

Performance engineering

Platform engineering

AI in engineering

Leadership

Stakeholder management

Cloud infrastructure

Data analytics

Education

Bachelor's degree in CS/Engineering

Tools

Observability platforms

Production analytics

Job description

ServiceNow seeks a Director to lead the Data & Storage Reliability Engineering organization, improving reliability, observability, performance, scalability, and customer experience across database, storage, and platform infrastructure.

The role builds and scales engineering teams focused on reliability, observability, diagnostics, automation, and migration readiness, while driving systemic improvements and the adoption of AI-powered tooling.