MS SQL Architect

·         Deep SQL Server architecture & internals — query engine, storage engine, transaction/locking/isolation levels, execution plan internals; 15+ yrs hands-on, architect-level design authority (not admin-level)

·         HA/DR design mastery — Always On AGs, clustering, replication topologies; able to architect DR strategy across on-prem VMs and hybrid cloud, not just configure it

·         Cloud migration engineering — hands-on RDS for SQL Server (and/or Azure SQL/MI) migration mechanics: DMS, native backup/restore, licensing model trade-offs (BYOL vs. License Included)

·         Strong Python scripting — automation, tooling, and integration development; not "can write a script" but production-grade automation engineering

·         AI-assisted engineering fluency — daily hands-on use of AI coding assistants (GitHub Copilot, Claude Code, Cursor, or similar) to accelerate schema design, query optimization, and code review

·         LLM/agentic infrastructure exposure — working knowledge of embeddings, vector databases, RAG, and semantic search as they intersect with data platform design 

·         AI-enabled database operations — experience evaluating or building AI-driven observability, anomaly detection, or self-healing/auto-remediation tooling for database platforms

·         MCP / tool-integration awareness — familiarity with Model Context Protocol or comparable frameworks for connecting AI agents to data systems 

·         Performance engineering at scale — wait-stats methodology, indexing strategy, parameter sniffing mitigation — demonstrable via live plan-reading, not resume claims