Lead onboarding, adoption, retention, and expansion for enterprise SaaS customers. Deliver fast time-to-value (≤14 days), run meaningful QBRs, feed product insights to engineering, surface upsell opportunities, secure references/case studies, and handle difficult customer conversations with empathy and clarity.

Design, build, and maintain scalable time-series data pipelines and connectors for thousands of buildings. Develop, train, and deploy ML models for energy and water analytics, troubleshoot data quality and integration issues, and collaborate with partners, developers, and customers to deliver production-ready AI solutions.

Build and maintain end-to-end features across frontend, backend, and data layers for an energy-efficiency platform. Improve workflows (audits, reporting, pricing), integrate and optimize connectors (Clickhouse, TimescaleDB, MQTT), contribute to architecture, and implement advanced UI components like Sankey diagrams and virtual meters.