• Architect and lead the evolution of our modern data platform, driving technical decisions on tooling, infrastructure patterns, and scalability strategies that support both traditional analytics and AI/ML workloads at scale • Design and build production LLM pipelines and infrastructure that power intelligent operations. • Own end-to-end data acquisition and integration architecture across diverse sources (CRMs, clickstream, third-party APIs), establishing patterns and frameworks that enable self-service data access while maintaining data quality and governance • Create shared abstractions and tooling for AI – for example, common prompt and tool patterns, logging and monitoring, and reusable components – so other engineers can build on a consistent foundation. • Shape our data and system architecture so AI can safely stitch together longitudinal signals across product, billing, support, and operations and recommend what should happen next, not just report what happened. • Lead by example in AI-augmented engineering, using AI to multiply your own speed, mentoring L2/L3 engineers, and raising the bar for how we design, ship, and operate AI-powered features. • Mentor and influence engineering culture, conducting design reviews, providing technical guidance to engineers across the organization, and championing data platform adoption and best practices