Senior Manager/Lead Systems & Data Architect

Say hello to opportunities. If you’re looking to be part of what’s next in communication, you’re in the right place. At RingCentral, we believe the best customer experiences happen when humans and AI work together. Our agentic voice AI portfolio—AIR, AVA, and ACE—brings together automation, assistance, and insights across the entire conversation lifecycle. The result? More seamless, intelligent experiences for businesses everywhere. With $2.5B+ in ARR and $250M invested in R&D annually, we’re building the future of AI-powered business communications. We are seeking a Sr. Manager/Lead Systems & Data Architect with deep hands-on technical expertise and strategic vision to design and lead the next generation of our enterprise intelligence data platform. This role will own the architecture, roadmap, and execution of a modern, cloud-native data ecosystem that supports analytics, real-time processing, and advanced AI/ML and LLM workloads at scale. This is a highly visible, high-impact role that sits at the intersection of data engineering, cloud infrastructure, analytics, and AI. You will serve as the technical authority for data architecture decisions while mentoring teams and influencing senior stakeholders across the organization. If you thrive in complex modernization initiatives, enjoy solving hard distributed systems problems, and want to shape a platform that will power data-driven and AI-enabled products for years to come, this role is for you. What You’ll Do Architecture & Strategy Define and own a cloud-native, enterprise-scale data architecture that supports batch, streaming, analytics, and AI/ML workloads. Develop and execute a multi-year data platform modernization roadmap, balancing delivery speed, risk, cost efficiency, and business continuity. Drive architectural decisions across Snowflake, Databricks, and cloud-native services, ensuring scalability, security, and performance. Platform & Engineering Leadership Partner closely with data engineering, analytics, infrastructure, security, and data science teams to design scalable ingestion, transformation, and serving layers. Define standards and best practices for data ingestion, transformation, integration, and data modeling across diverse structured and semi-structured sources. Establish robust data governance, quality, lineage, and access control frameworks aligned with enterprise and regulatory needs. Optimize performance and cost efficiency across Snowflake and Databricks environments, including workload isolation, query optimization, and storage strategies. DevOps, Reliability & Security Lead the adoption of DevOps, CI/CD, and Infrastructure as Code practices (e.g., Terraform) for data platforms. Define patterns for environment management, observability, disaster recovery, and security-by-design. Ensure the platform meets enterprise standards for data privacy, compliance, and resilience. AI / ML & Future Readiness Design the data…