Lead Revenue and Data Analyst

Responsibilities

• Support ARR modelling, forecasting, and variance analysis across the business
• Develop and maintain customer account stratification frameworks (by size, industry, engagement, health score, etc.)
• Monitor customer activities and engagement patterns to identify trends and opportunities
• Analyze drivers of New Logo ARR, Expansion, Contraction, and Churn
• Build predictive models for revenue forecasting and customer behavior
• Build and maintain KPI metrics including:
  Annual Recurring Revenue (ARR), Net Revenue Retention (NRR), Gross Revenue Retention (GRR), Customer Acquisition Cost (CAC), and Lifetime Value (LTV)
• Design and implement dashboards and reporting systems that serve as the single source of truth for business performance
• Build and maintain executive dashboards using Power BI and other BI tools
• Ensure data accuracy and consistency across all revenue systems (CRM, billing, product analytics, etc.)
• Create board‑ready reporting packages for executive leadership
• Establish and document data governance standards and best practices
• Perform data validation, reconciliation, and anomaly detection across systems
• Highlight growth opportunities, risk areas, and efficiency improvements
• Conduct cohort analyses, retention studies, and revenue waterfall analyses
• Partner with Sales, Customer Success, Product, and Finance teams to drive data‑informed decisions
• Support optimization of end‑to‑end SaaS reporting
• Partner with Rev Ops and Sales leadership to ensure metric alignment and data accuracy
• Develop and maintain documentation for data definitions, metrics, and processes
• Continuously improve data collection, processing, and reporting workflows

• Experience in revenue operations, business intelligence, or analytics roles, preferably in SaaS/subscription businesses
• Advanced proficiency in Excel/Google Sheets and SQL
• Experience with at least one major BI tool
• Familiarity with CRM and billing platforms
• Understanding of data warehousing concepts and ETL processes
• Deep understanding of SaaS business models and key metrics (ARR, CAC, LTV, churn, expansion, net dollar retention, etc.)
• Strong quantitative skills with ability to derive insights from complex datasets
• Excellent ability to present data stories to both technical and non‑technical audiences
• Experience partnering with cross‑functional teams including Sales, Customer Success, Product, and Revenue Operations