Staff Data Engineer

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.

Role summary

The Staff Data Engineer designs, builds, and operates the data foundation and the evaluation infrastructure behind ServiceNow CRM Agentic AI. The role has two halves that reinforce each other. The first is conventional but demanding data engineering: architecture, pipelines, and transformation across structured and unstructured sources, held to production standards of reliability and quality. The second is newer and rarer: building the measurement layer that tells product and AI teams whether an AI agent actually did its job.

That second half changes the nature of the work. Agent behavior is probabilistic, so quality cannot be asserted, only measured-against metrics that have to be defined before they can be tracked, and against ground truth that someone has to establish and defend. This engineer defines those metrics with product and AI teams, sets the labeling and validation standards, builds the datasets that reflect how agents behave in the real world, and automates the pipelines and dashboards that turn agent execution logs into a signal the organization can act on.

the Staff Data Engineer owns these decisions across a domain rather than within a single project. They set design standards and quality gates others adopt, mentor junior engineers, and generalize evaluation patterns so each Agentic AI team is not rebuilding measurement from scratch.

What you do Design and architect data infrastructure

Design and oversee deployment of the data architecture and pipelines that capture, manage, and store structured and unstructured data from internal and external sources. Establish the processes and data flows across cloud services, local databases, and other applicable storage forms, and own the contracts between data producers and consumers.

Build and automate data transformation

Develop technical tools using machine learning and data-engineering techniques to cleanse, organize, and transform data. Implement automated processes that maintain the integrity of data structures and hold quality standards on an ongoing basis rather than at a point in time.

Define agentic evaluation metrics and ground truth

Partner with product and AI teams to define evaluation metrics for agentic workflows, including task and mission completeness, instruction adherence, tool use, and end-to-end workflow success. Establish ground truth labeling standards, annotation guidelines, and validation criteria, and design evaluation datasets that reflect real-world agent execution rather than idealized paths.

Build agentic evaluation pipelines

Design and implement automated evaluation infrastructure that measures AI agent performance using LLMs and agent execution logs. Create the dashboards, reporting, versioning, and reproducibility that make evaluation datasets and results trustworthy over time and comparable across releases.

Establish standards and continuous improvement

Create design standards and quality assurance processes for data systems. Define quality gates and validation frameworks, analyze workflow performance, and recommend optimizations that keep the platform aligned with evolving CRM AI requirements.

Lead cross-functional collaboration

Collaborate with product, engineering, and data science teams. Mentor junior engineers on data engineering and evaluation design, and generalize evaluation patterns and metrics so they can be reused across Agentic AI products rather than rebuilt per team.

Required qualifications

• Data engineering depth. 8+ years in data engineering, with a record of owning production data platforms end-to-end. Depth and demonstrated judgment matter more than tenure.
• Domain ownership. Demonstrated ownership of a data domain or platform, including architecture decisions, migrations, and the operational consequences of both.
• Evaluation infrastructure experience. Hands-on experience building measurement or evaluation infrastructure for machine learning or AI systems: evaluation pipelines, benchmark harnesses, quality dashboards, or golden datasets. This is the gating requirement for the role.
• Ground truth and labeling. Experience defining ground truth and running or directing labeling work, including guideline authoring and annotator agreement.
• Core technical stack. Production Python, strong SQL, distributed processing, workflow orchestration, a cloud data platform, and infrastructure-as-code, with continuous integration and on-call experience.
• Standard setting. Evidence of setting standards that others adopted, such as design standards, quality gates, or validation frameworks, rather than only meeting standards already in place.
• CRM and Order domain familiarity. Sales CRM, lead-to-cash, or order data familiarity, at a depth sufficient to judge whether an evaluation dataset reflects how sellers and agents actually work.
• Internal programs. Demonstrated experience in internal evaluation programs and patterns such as automated evaluation suites, data kits, or AI data factory approaches.
• Mentorship and generalization. Mentorship of junior engineers, and a record of generalizing a solution so that it was reused across teams or products.
• Education. Bachelor's degree in computer science, engineering, or a related technical field, or equivalent practical experience.

Work Personas

We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.

Equal Opportunity Employer

ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.

Accommodations

We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact [email protected] for assistance.

Export Control Regulations

For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.

From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.