Lead Systems & Data Architect
RingCentral · Belmont, CA · 3 wk ago
Engineering$177k–$253k/yrFull-time
About the role
RingCentral is a $2 Billion annual revenue company with 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. This role involves modernizing our enterprise data ecosystem, supporting analytics, real-time processing, and advanced AI/ML and LLM workloads at scale.
Responsibilities
- 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.
- 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.
- 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.
- 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.
- 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.
Requirements
- 10+ years of experience in data architecture, data engineering, or platform engineering roles.
- Proven success leading large-scale data platform migrations, including: On-prem data warehouses (e.g., Oracle, Teradata) to Snowflake, Hadoop ecosystems (HDFS, Hive, Spark) to Databricks.
- Strong experience designing and operating data platforms on AWS, Azure, or GCP, including compute, storage, networking, and security.
- Solid background in data governance, metadata management, lineage, and cataloging (e.g., Purview, Collibra, Alation).
- Experience with real-time and streaming architectures (Kafka, Kinesis, Pub/Sub) and orchestration tools (Airflow, dbt).
- Demonstrated ability to lead cross-functional teams and complex migration programs.
Qualifications
- Deep hands-on technical expertise and strategic vision.
- Exceptional communication skills and executive-level presence.
- Strong experience with SQL, Spark, and Python.
- Strong experience with distributed data processing.
- Experience with real-time and streaming architectures.
- Experience with real-time and streaming architectures.
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