Senior Staff Data Platform Engineer - Data Access Team

ServiceNow•Santa Clara, CA

29d•$201,300 - $352,300•Hybrid

About The Position

The Senior Staff Data Platform Engineer will serve as a key technical leader in ServiceNow’s Platform Persistence group, building and guiding the data access layer that enables applications, workflows, and AI agents to retrieve data quickly, correctly, and securely. The role focuses on the ServiceNow ORM and data access layer, which translates the platform’s logical object model into high-performance queries across heterogeneous database engines, including RaptorDB (PostgreSQL), MariaDB, Oracle, Trino, graph databases, and time-series databases. The engineer will set architectural direction as the data access layer expands to support new engines and AI-driven workloads. This includes solving challenges involving query translation, IO, transactions, concurrency, locking, reliability, and performance at scale. The position owns features from design through production, shapes roadmaps with product and engineering leadership, and promotes engineering craftsmanship through technical leadership, design and code reviews, testing strategy, production troubleshooting, and mentorship. This is a flexible hybrid position requiring two days per week in a ServiceNow office. Listed office locations include San Francisco, Pleasanton, Santa Clara, and San Diego, California, and Kirkland, Washington.

Requirements

12 or more years of software development experience with a bachelor's degree; or 8 or more years with a master's degree; or 5 or more years with a PhD; or equivalent work experience.
Demonstrated technical leadership at Staff or Senior Staff level, or equivalent.
Expert-level core Java development skills.
Advanced-to-expert backend platform development experience.
Expert understanding of object-oriented and modularized software best practices, especially in Java.
Knowledge of or experience with relational databases such as PostgreSQL, Oracle, MySQL, or Trino.
Strong knowledge of data structures, algorithms, object-oriented design, design patterns, and performance optimization at scale.
Experience with automated testing frameworks such as JUnit, Selenium, or TestNG, and integrating tests into CI/CD pipelines.
Deep understanding of software quality principles, including reliability, observability, and production readiness.
Good understanding of cloud-based and distributed systems.
Experience leveraging AI or critically considering how to integrate AI into engineering work, such as using AI-powered coding and operational tools, automating workflows, or assessing how AI changes software and infrastructure development.
Aptitude for quickly learning new technologies.
Demonstrated success delivering complex cross-team projects on time and with quality.
Experience troubleshooting difficult production issues, such as memory leaks, concurrency or locking issues, network problems, and intermittent failures across the stack.
Track record of influencing architecture and engineering decisions across multiple teams without relying on direct authority.
Strong written and verbal communication skills, including authoring design documents or RFCs and presenting technical strategy to senior engineering leadership.

Nice To Haves

Familiarity with the ServiceNow platform.
Familiarity with RDBMS internals.
Familiarity with Neo4j and graph databases.
Passion for database technologies.
Experience with Unix shell.
Experience working in a DevOps environment.
Experience working in a customer-focused environment.

Responsibilities

Set the technical direction and architecture strategy for the data access layer, balancing long-term scalability with delivery.
Work on core data architecture used throughout the product, ensuring it is high-quality, reliable, secure, and fast.
Own features from design through delivery, translating business requirements into well-architected, cross-team solutions.
Lead design and code reviews, promoting performance, maintainability, and testability across multiple teams.
Define comprehensive functional, regression, integration, and performance testing strategies.
Diagnose and resolve difficult production issues across the stack.
Mentor engineers at every level and raise the technical bar across the organization.
Influence architecture and engineering decisions across teams and communicate technical strategy to senior engineering leadership.