Senior Manager, Data Engineering
$190,100–$190,100 year
On-siteBelmont, California, United States
Job Summary
Lead a team of data engineers to build and scale the revenue data mart powering RingCentral's $2.5B+ ARR platform. Architect and oversee pipelines integrating data across Hadoop, Oracle, Snowflake, AWS S3, and PostgreSQL, ensuring reliable delivery for finance, sales, and executive reporting. Mentor staff, manage hiring, and partner with Finance and Product stakeholders to drive technical strategy, enforce governance, and optimize cloud costs. Handle incident management, root-cause resolution, and introduce ML/AI techniques to improve pipeline reliability. Manage sprint planning using Agile/Scrum practices while communicating tradeoffs to senior leadership.
Required Qualifications
- 10+ years of experience in data engineering
- 3+ years in a management or technical leadership role
- Proven experience managing a team of data engineers across varying experience levels
- Hands-on expertise with Hadoop ecosystem tools (Hive, Spark, HDFS)
- Hands-on expertise with Oracle
- Hands-on expertise with Snowflake
- Hands-on expertise with AWS S3
- Hands-on expertise with PostgreSQL
- Strong background building data marts or warehouses, ideally supporting finance or revenue use cases
- Deep understanding of ETL/ELT design
- Deep understanding of dimensional data modeling
- Deep understanding of orchestration tools (e.g., Airflow, Control-M)
- Solid grasp of SQL performance tuning
- Solid grasp of large-scale data processing
- Experience with cloud data architecture
- Experience with cost/performance optimization on AWS
- Excellent stakeholder management skills across Finance, Analytics, and Engineering
- Experience with Agile/Scrum delivery
- Working knowledge of business systems that feed or consume revenue data, such as Marketo, Salesforce, Anaplan, and NetSuite
- Familiarity with BI tools such as Tableau and Sigma
- Familiarity with how data mart design impacts downstream reporting
- General knowledge of ML/AI concepts (e.g., predictive models, anomaly detection)
- General knowledge of how ML/AI concepts apply to data pipelines and analytics
Desired Qualifications
- Experience with revenue recognition, billing systems, or subscription/SaaS revenue data
- Familiarity with data governance, lineage, and cataloging tools
- Experience with CI/CD for data pipelines (e.g., dbt, Jenkins, GitHub Actions)
- Background in a high-growth SaaS or telecom environment
- Experience migrating on-prem systems (Hadoop/Oracle) to cloud-native platforms (Snowflake/AWS)
- Direct experience building integrations from Marketo, Salesforce, Anaplan, or NetSuite into a data warehouse
- Experience incorporating ML/AI capabilities into data platforms, such as automated data quality checks or forecasting models
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