• Build, optimize, and maintain ELT/ETL pipelines that ingest, clean, and organize operational data from ServiceNow, mainframe environments, distributed systems, and cloud platforms. • Develop robust extraction, transformation, and ingestion patterns for ServiceNow operational data (incidents, alerts, changes, requests), ensuring it is reliable, well-modeled, and ready for AI/ML use cases. • Design scalable data models, storage frameworks, and integration layers in Snowflake and related modern data platforms, with an emphasis on performance, reliability, and operational relevance. • Implement data quality standards, monitoring, validation, and lineage to ensure pipelines produce clean, trustworthy, and auditable datasets. • Partner with Data Scientists, ML Engineers, and MLOps to deliver and maintain production-grade feature pipelines, training datasets, and inference-ready data, supporting predictive models, anomaly detection, and intelligent runbooks. • Support ML production workflows by enabling model registration, versioning, and lifecycle management within Snowflake, working alongside ML and MLOps teams (model development itself is not the primary responsibility of this role). • Integrate Snowflake AI components, including Cortex LLMs, into data workflows for use cases such as enrichment, summarization, and operational intelligence. • Identify opportunities to streamline data workflows, reduce manual intervention, and lower operational costs while improving reliability and scalability. • Work with Finance, Procurement, Cloud Operations, Mainframe Operations, and Service Operations teams to ensure data products align with high-value business outcomes.