A Fortune 500 financial data company needed to migrate a complex SQL Server environment to Google Cloud Platform while maintaining strict data security standards and accelerating delivery timelines. The scale of data translation work required was estimated at thousands of manual engineering hours — a timeline and cost the program could not absorb using traditional methods.
Conducted a data architecture assessment to understand the full scope of SQL Server schemas, stored procedures, and data relationships before any migration work began.
Designed the Google Cloud Platform target architecture with data quality, security, and AI-readiness as primary constraints — not afterthoughts.
Built a structured data foundation that enabled LLM-based automation: standardized schemas, consistent naming conventions, and documented transformation rules.
Developed and deployed LLM-driven automation scripts to handle repetitive data translation tasks — SQL schema conversions, data type mappings, and transformation logic that had previously required manual engineering.
Maintained zero data security incidents throughout the program through rigorous access controls, audit logging, and encrypted data-in-transit standards.
Delivered the program ahead of the original timeline due to the efficiency gains from AI automation.
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