Staff Data Engineer

ServiceNow, Inc.; Hyderabad; On-site; INR 4,200,000 - 7,000,000; Full time

Job summary

ServiceNow, Inc. is hiring a senior data engineer to design and implement production data pipelines, data warehousing, and ETL/ELT systems. You will lead data architecture, partner with product and AI teams, and ensure data quality across internal and external sources.

You will build ML data tools, establish evaluation metrics, and develop automated pipelines for agentic AI workflows, using Now LLM Service models and logging. Strong cloud, governance, and collaboration experience are essential.

Qualifications

• 9+ years designing and building production data pipelines, data warehousing, ETL/ELT systems; strong expertise in SQL, Python, Javascript or equivalent, and distributed data processing
• 3+ years working on ML systems, model validation infrastructure, automated testing, or AI-powered applications; hands-on experience with data quality, feature engineering, and model monitoring
• Proven track record deploying, monitoring, and maintaining large-scale data systems in production; experience troubleshooting data quality issues, latency, and system reliability
• Ability to design scalable, secure data architectures; experience with cloud platforms (GCP, AWS), APIs, event-driven systems, and data governance patterns
• Familiarity with AI/ML evaluation frameworks, LLM-based systems, or agentic workflows; ability to instrument and evaluate complex AI systems; understanding of model performance tracking
• Comfortable driving technical conversations across multiple teams; takes end-to-end ownership; works autonomously; mentors peers on data engineering standards and best practices
• Experience on ServiceNow platform is good to have.

Responsibilities

• Design and oversee deployment of data architecture and pipelines to capture, manage, and store structured and unstructured data from internal and external sources; establish processes and data flows using cloud, local databases, and applicable storage forms
• Build technical tools using ML and data-engineering techniques to cleanse, organize, and transform data; implement automated processes to maintain data structure integrity and quality standards on an ongoing basis
• Define evaluation metrics for agentic workflows; establish ground truth labeling standards and validation criteria; design evaluation datasets that reflect real-world agent execution
• Build automated evaluation pipelines to measure AI agent performance using Now LLM Service models and execution logs; create dashboards, reporting, versioning, and reproducibility of evaluation datasets and results
• Establish standards and quality assurance processes for data systems; define quality gates and validation frameworks; analyze workflow performance and recommend optimizations
• Lead cross-functional collaboration with product, engineering, and data science teams; mentor junior engineers on data engineering standards; generalize evaluation patterns and metrics across Agentic AI products
• Experience and outcome driven data engineering; demonstrate end-to-end ownership and autonomy

Skills

Data pipelines

Data warehousing

ETL/ELT

SQL

Python

JavaScript

Distributed data processing

ML systems

Model validation

Tools

GCP

AWS

Job description

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.

• Design & Architect Data Infrastructure — Design and oversee deployment of data architecture and pipelines to capture, manage, and store structured and unstructured data from internal and external sources; establish processes and data flows using cloud, local databases, and applicable storage forms

• Build & Automate Data Transformation — Develop technical tools using ML and data-engineering techniques to cleanse, organize, and transform data; implement automated processes to maintain data structure integrity and quality standards on an ongoing basis

• Define Agentic Evaluation Metrics & Ground Truth — Partner with product and AI teams to define evaluation metrics for agentic workflows (task completeness, tool use, workflow success); establish ground truth labeling standards and validation criteria; design evaluation datasets that reflect real-world agent execution

• Build Agentic Evaluation Pipelines — Design and implement automated evaluation infrastructure that measures AI agent performance using Now LLM Service models and execution logs; create dashboards, reporting, versioning, and reproducibility of evaluation datasets and results

• Establish Standards & Continuous Improvement — Create design standards and quality assurance processes for data systems; define quality gates and validation frameworks; analyze workflow performance and recommend optimizations to accommodate 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; generalize evaluation patterns and metrics across Agentic AI products

• 9+ years designing and building production data pipelines, data warehousing, ETL/ELT systems; strong expertise in SQL, Python, Javascript or equivalent, and distributed data processing

• 3+ years working on ML systems, model validation infrastructure, automated testing, or AI-powered applications; hands-on experience with data quality, feature engineering, and model monitoring

• Proven track record deploying, monitoring, and maintaining large-scale data systems in production; experience troubleshooting data quality issues, latency, and system reliability

• Ability to design scalable, secure data architectures; experience with cloud platforms (GCP, AWS), APIs, event-driven systems, and data governance patterns

• Familiarity with AI/ML evaluation frameworks, LLM-based systems, or agentic workflows; ability to instrument and evaluate complex AI systems; understanding of model performance tracking.

• Comfortable driving technical conversations across multiple teams; takes end-to-end ownership; works autonomously; mentors peers on data engineering standards and best practices

• Experience on ServiceNow platform is good to have.

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 (https://careers.servicenow.com/life-at-servicenow#workpersonas) . 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 [e-mail] 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.

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