Sr. Data Analyst, Finance

We are looking for a Senior Data Analyst to help scale the analytics foundation that powers our revenue engine. In this high-impact role, you will translate complex business questions into trusted data models, dashboards, and insights that improve decision- making across Sales, Marketing, Customer Success, and Revenue Operations. You will be hands-on in Snowflake and Salesforce while partnering cross-functionally to define core SaaS metrics, strengthen data quality, and create scalable reporting standards for the business.

• Own revenue data architecture: Design, maintain, and improve scalable Snowflake schemas that support accurate reporting and analytics across the customer lifecycle.
• Define trusted business metrics: Partner with Revenue Operations and Finance to standardize key SaaS metrics such as ARR, NRR, retention, pipeline, bookings, and customer lifetime value.
• Improve data quality and governance: Establish documentation, definitions, QA checks, and reporting standards that increase trust in revenue data.
• Model Salesforce data: Map, extract, transform, and document complex Salesforce data structures, including standard and custom objects, to support reliable reporting from reusable frameworks, scripts and tools in Python, SQL or AI to automate recurring analyses, improve data access, to scale the team’s capabilities
• Build actionable dashboards: Create clear, intuitive dashboards that help leaders and operators monitor performance, identify risks, and act quickly.
• Align with technology and systems infrastructure teams: Partner with data engineering and technology teams to define data requirements, ensure data quality, and build robust pipelines that support reliable, scalable analytics across the organization and systems.
• Deals Desk Support: Serve as a resource for Deals Desk and system administrators to process complex deal structures in Salesforce to ensure data accuracy and usability
• Lead through influence: Collaborate with analysts and business partners across teams to align on best practices, shared definitions, and scalable analytics processes.
• Use AI to work smarter: Apply modern AI tools where appropriate to accelerate analysis, documentation, SQL development, and workflow automation.