AI-Native Engineering: Lead the adoption of AI across the entire engineering lifecycle—from product strategy, PRDs, UX, architecture, implementation, code reviews, debugging, testing, security, CI/CD, deployment, observability, APM, operations, and documentation—to improve engineering productivity, software quality, reliability, customer experience, and delivery velocity. Strong understanding of database schema design, data modeling, and modern data platforms, including relational, graph, vector, columnar, OLAP, and streaming data architectures, with the ability to design scalable data models for AI and enterprise workloads.