The use of Snowflake as a cloud-native data warehouse has dramatically changed the management of analytics workload for Property and Casualty (P&C) insurers, while simultaneously presenting serious cost governance challenges. The heavy volume of searches, big data retention, and decentralized business intelligence operations are industry-standard procedures that tend to lead to uncontrolled credit usage and overspending on storage. This research introduces a modular five-layer optimization framework focused on property and casualty insurance data, combining workload segmentation, and compute sizing with Snowflake's account usage metadata. The framework is tested and validated using Kaggle’s Insurance Agency Data, representing real-world P&C operations across 17 states. Benchmark queries simulating core insurance workloads were designed using modified TPC-H logic, a standard decision support benchmark that enables realistic performance evaluation under analytical query conditions, achieving up to 82% cost reduction and a 64% reduction in execution time without compromising the results. These results highlight the efficiency of the framework to facilitate proactive and elastic cost control. Future studies can investigate AI-driven query forecasting, scalable warehouse dynamics, and real-time anomaly detection to further advance cloud-native data ecosystem governance.

Snowflake Cost Optimization, Property & Casualty Insurance Data Workloads, Metadata-Driven Cost Control, Query Performance Tuning

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