Staff Data Engineer

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 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.

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