Senior Manager, Data Engineering

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 looking for a Senior Manager of Data Engineering to lead a team of data engineers, from junior to senior level, responsible for building and scaling the data infrastructure that powers RingCentral's revenue platform. This role owns the design, development, and delivery of a revenue data mart into a reliable, well-governed source of truth for finance, sales, and executive reporting.

This is a hands-on leadership role: you'll set technical direction, mentor and grow engineers at all levels, and partner closely with Finance, Revenue Operations, Analytics, and Product stakeholders to ensure the data mart meets business needs for accuracy, timeliness, and scalability.

What You'll Do

• Lead, mentor, and grow a team of data engineers spanning junior to senior levels, including hiring and performance management.

• Own the technical strategy and roadmap for the revenue data mart, supporting revenue recognition, billing, forecasting, and executive reporting.

• Architect and oversee data pipelines integrating and transforming data across Hadoop, Oracle, Snowflake, AWS S3, and PostgreSQL.

• Ensure reliable data integration with business systems such as Marketo, Salesforce, Anaplan, and NetSuite, maintaining consistency across the revenue lifecycle.

• Partner with BI teams to structure the data mart for reporting and dashboarding in tools such as Tableau and Sigma.

• Identify opportunities to apply ML/AI techniques (e.g., anomaly detection, forecasting) to improve pipeline reliability and revenue insights.

• Drive engineering best practices: data modeling, ETL/ELT design, orchestration, testing, CI/CD, and data quality/observability.

• Partner with Finance, Revenue Operations, and Analytics to translate business requirements into scalable data solutions.

• Manage sprint planning, prioritization, and delivery using Agile/Scrum practices.

• Establish and enforce data governance, security, and compliance standards (e.g., SOX considerations).

• Optimize pipeline performance and cost across on-prem (Hadoop, Oracle) and cloud (Snowflake, AWS) environments.

• Own incident management and root-cause resolution for pipeline issues impacting revenue reporting.

• Evaluate and introduce new tools and architectural patterns to modernize the data platform.

• Communicate progress, risks, and technical tradeoffs to senior leadership and non-technical stakeholders.

What We're Looking For

Required:

• 10+ years of experience in data engineering, with 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), Oracle, Snowflake, AWS S3, and PostgreSQL.

• Strong background building data marts or warehouses, ideally supporting finance or revenue use cases.

• Deep understanding of ETL/ELT design, dimensional data modeling, and orchestration tools (e.g., Airflow, Control-M).

• Solid grasp of SQL performance tuning and large-scale data processing.

• Experience with cloud data architecture and 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, and how data mart design impacts downstream reporting.

• General knowledge of ML/AI concepts (e.g., predictive models, anomaly detection)