Revenue Analytics Engineer

About Lansweeper Lansweeper is a leading IT asset management company that helps organizations gain complete visibility into their IT landscape. Our technology discovers, inventories, and manages every IT asset across on-premises, cloud, and IoT environments. As we grow through new products and market expansion, revenue analytics is becoming critical to steer the business forward. What Success Looks Like C-level decisions to optimize growth are based on revenue metrics and insights, as they are highly trusted facts on the evolution of the business Revenue metrics are structured so they are relevant for board-level insights Key trends in revenue metrics are explained by linking back to sales and finance processes, ensuring the right strategic decisions for growing the company are taken Forecasting for key revenue metrics is in place and used to steer go-to-market actions The Real Challenge The sales organization is moving quickly and needs revenue facts to understand the success of its campaigns and to plan new sales plays The market is changing and so is our product — we need to allocate sales and marketing efforts where it matters most for growth Revenue metrics are sourced from multiple systems, have historic complexity due to acquisitions and system migrations, and data quality varies over time What You Will Do Build, maintain, and improve the revenue data models that power executive-level reporting and board-ready metrics Reconcile revenue data across systems of record (CRM, billing, ERP) and ensure a single source of truth for financial KPIs Design and deliver dashboards and reports that translate complex revenue data into clear, actionable insights for sales, finance, and leadership Partner with sales operations and finance to understand changing business processes and reflect them accurately in revenue analytics Develop and maintain forecasting models for key revenue metrics to support go-to-market planning Investigate and explain trends, anomalies, and shifts in revenue data, linking them back to underlying business drivers Proactively improve data quality and integrity across revenue-related data pipelines Required Experience with financial metrics reporting — you know how revenue, bookings, churn, and related KPIs are defined and measured Understanding of subscription sales processes — you are familiar with concepts like ARR, MRR, expansion, contraction, and renewal cycles Strong SQL skills — you can write, optimize, and debug complex queries against large datasets Experience in reconciling systems of record — you have dealt with data mismatches between CRM, billing, and finance systems and know how to resolve them Analytical mindset with the ability to translate data into business narratives that support decision-making Nice to Have Experience with SaaS KPI reporting (e.g., net revenue retention, LTV, CAC payback) Hands-on experience with Snowflake, dbt, and/or Power BI Familiarity with data modeling best practices (dimensional modeling, slowly changing dimensions)