Lead Systems & Data Architect

Overview

Say hello to opportunities. It s not every day that you consider starting a new career. We re Ring Central, and we re happy that someone as talented as you is considering this role. Ring Central is a $2 Billion annual revenue company with double digit Annual Recurring Revenue (ARR) and a $93 Billion market opportunity in UCaaS, Contact Center and AI-powered adjacencies.

We invest more than $250 million annually to ensure our AI-enabled technology and platforms meet or exceed the needs of our customers. Ring Sense AI is our proprietary AI solution designed to fit the business needs of our customers, orchestrated to be accurate and precise, and built on the same open platform principles we apply to our core software solutions.

This is where you and your skills come in. We are embarking on a transformative initiative to modernize our enterprise data ecosystem. This modernization will serve as the foundation for our next-generation analytics, machine learning, and AI capabilities.

We are seeking a Lead Systems & Data Architect with deep hands-on technical expertise and strategic vision to design and lead the next generation of our enterprise 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 s 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.

• 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.

• Dev Ops, Reliability & Security

  • Lead the adoption of Dev Ops, 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 foundation required for advanced analytics, ML, and LLM workloads, including feature stores, vector storage, and real-time data access.

  • Stay ahead of emerging trends in AI, ML, and data platforms, translating innovation into practical, production-ready architectures.

  • Influence the evolution toward lakehouse, data mesh, and data product-oriented architectures where appropriate.

• Leadership & Influence

  • Provide technical leadership and mentorship to senior data engineers and architects.

  • Act as a trusted advisor to engineering leadership and business stakeholders.

  • Clearly communicate complex architectural concepts to both technical and non-technical audiences.

• 10+ years of experience in data architecture, data engineering, or platform engineering roles.

• Proven success leading large-scale data…