The workflow aims to help a Data Team Lead transform raw source table schemas and business requirements into well‑structured semantic models for dbt projects. It ensures that metrics have a single source of truth, models follow medallion or Kimball principles, and documentation and testing standards are consistently applied. The output includes a semantic model diagram, dbt project structure, metric definitions, and documentation guidelines.

The goal is to analyze cloud data warehouse usage and identify ways to reduce spend without compromising reliability or breaching contractual limits. The workflow must decompose cost drivers, propose safe optimization levers, and deliver a clear, ROI‑focused plan. Recommendations must include required approvals, rollback paths, and monitoring guardrails.

The goal is to automate the creation of data quality checks for fintech lending and risk pipelines. The system must analyze schemas, business rules, historical patterns, and SLAs to produce a production-ready DQ test suite, alerting plan, runbook, and exception-handling policy. Outputs must align with regulatory-grade accuracy, deterministic test behavior, and low operational overhead.