Senior Manager, Products - Data Science & Analytics

Position: Senior Manager, Payment Products - Data Science & Analytics
About Rippling

Rippling gives businesses one place to run HR, IT, and Finance. It brings together all of the workforce systems that are normally scattered across a company, like payroll, expenses, benefits, and computers. For the first time ever, you can manage and automate every part of the employee lifecycle in a single system.

Take onboarding, for example. With Rippling, you can hire a new employee anywhere in the world and set up their payroll, corporate card, computer, benefits, and even third-party apps like Slack and Microsoft 365-all within 90 seconds.

Based in San Francisco, CA, Rippling has raised $1.4B+ from the world's top investors-including Kleiner Perkins, Founders Fund, Sequoia, Greenoaks, and Bedrock-and was named one of America's best startup employers by Forbes.

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About the role

Rippling is looking for a Senior Manager, Payment Products Data & Analytics to design, build, and maintain services that automatically process massive amounts of financial data, providing visibility into each step of the money movement lifecycle in Rippling's payments product ecosystem. This role sits within the Finance organization and partners closely with cross-functional leaders across Finance, Legal & Compliance, Product, and Operations to drive meaningful business impact.

This leader will manage and grow a team of data scientists and analysts, owning the strategy, vision, and roadmap for payments data and analytics. This critical role demands a leader who can leverage deep financial acumen with an advanced understanding of technology - including Artificial Intelligence (AI), data architecture, and Business Intelligence (BI) - to deliver 100% accuracy, efficiency, and compliance for our accounting and finance functions.

What you will do

* Own the Data Strategy:
Define and execute the long-term vision and roadmap for Rippling's payment data ecosystem, ensuring it scales alongside our rapidly expanding product suite.

* Architect Financial Integrity:
Design and maintain the data foundation for all money movement-including payroll, taxes, and benefits-ensuring 100% accuracy and "single source of truth" reliability.

* Automate Reconciliation:
Build sophisticated, automated systems to reconcile millions of daily transactions, reducing manual intervention and accelerating the financial close process.

* Engineer for Auditability:
Develop "audit-ready" data architectures and rigorous documentation that satisfy both internal controls and external regulatory requirements

* Partner Cross-Functionally:
Act as the technical bridge between Product, Engineering, and Finance, translating complex regulatory and accounting needs into durable technical requirements.

* Optimize Data Pipelines:
Oversee the health and performance of end-to-end ELT pipelines, ensuring data is not only accurate but highly performant and accessible for real-time decision-making.

* Drive AI

Innovation: Identify opportunities to leverage AI and advanced modeling to detect payment anomalies, predict cash flow trends, and optimize money movement efficiency.

* Build & Mentor:
Lead a high-performing team of data scientists and analysts, fostering a culture of technical excellence, accountability, and a "bias for action.

What you will need

* 8+ years of experience in Data Engineering, Analytics, or Financial Systems. Fintech or SaaS experience is a must, specifically dealing with complex "multi-hop" payment flows (e.g., Ledgering, Sub-ledgers, and flow-of-funds).

* Bachelor's or Master's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics) or equivalent technical experience.

* Mastery of SQL and Python is non-negotiable. Deep expertise in modern data stack tools like dbt, Snowflake/Big Query, and Airflow to set the technical bar for the team.

* Proven ability to design data models that reflect GAAP accounting principles, automated bank reconciliations, and regulatory reporting requirements.

* Demonstrated success in building a data function from the ground up, moving from reactive to a proactive, multi-quarter strategic…