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RingCentralPosted 1 week ago

Senior Manager/Lead Systems & Data Architect

$184,600–$184,600 year

On-siteBelmont, California, United States

Full TimeSenior LevelLarge

Job Summary

Design and own a cloud-native, enterprise-scale data architecture supporting batch, streaming, analytics, and AI/ML workloads. Develop a multi-year modernization roadmap balancing delivery speed, risk, and cost efficiency while driving decisions across Snowflake, Databricks, and cloud-native services. Partner with engineering, analytics, and data science teams to define standards for ingestion, transformation, and governance, ensuring robust data quality and lineage. Lead DevOps, CI/CD, and Infrastructure as Code adoption to establish environment management, observability, and security-by-design patterns. Act as a trusted advisor to executive stakeholders, communicating complex architectural concepts to both technical and non-technical audiences.

Required Qualifications

  • 10+ years of experience in data architecture, data engineering, or platform engineering roles
  • Deep hands-on expertise with SQL, Spark, and Python, and strong understanding of distributed data processing
  • 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
  • Exceptional communication skills and executive-level presence

Desired Qualifications

  • Snowflake, Databricks, or cloud provider certifications (AWS, Azure, GCP)
  • Hands-on experience building ML and AI pipelines on Databricks or similar platforms
  • Experience with multi-petabyte-scale data platforms
  • Familiarity with data mesh, lakehouse, and domain-oriented data product concepts
  • Exposure to LLM pipelines, vector databases, or retrieval-augmented generation (RAG) architectures

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