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Payoneer

cross-border payments · global workforce management

Data Analyst Lead, RevOps (Gurugram)

IndiaPosted 1 month ago
Data Science And StatisticsSeniorFull Time; Regular
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About Payoneer Founded in 2005, Payoneer is the global financial platform that removes friction from doing business across borders, with a mission to connect the worlds underserved businesses to a rising global economy. Were a community with over 2,500 colleagues all over the world, working to serve customers, and partners in over 190 countries and territories. By taking the complexity out of the financial workflowsincluding everything from global payments and compliance to multi-currency and workforce management, to providing working capital and business intelligencewe give businesses the tools they need to work efficiently worldwide and grow with confidence. About The Role We are looking for a Lead Data, RevOps to drive data-backed decision-making across our GTM and Revenue Operations teams. This role sits at the intersection of data and business and requires someone who comes with a builder mindset to any problem statement thrown at them and scales, maintains and improves these recent and existing systems You will work closely with the growth team leadership and cross-functional teams to ensure that data is insightful, accurate, accessible, and actionable . Roles & Responsibilities - Own and manage business-critical data across Revenue, Operations, and GTM functions - Design and build dashboards and reports using BI tools (e.g., Power BI) to enable real-time visibility into key metrics - Analyze business performance across pipeline, revenue, and operational metrics to generate actionable insights - Partner with cross-functional teams (Sales, Marketing, Customer Success, Finance) to support data-driven decision making - Translate business problems into structured data analysis and scalable reporting solutions - Automate recurring reporting and workflows using tools like Excel, SQL, and Python to improve efficiency - Develop and maintain data models and ensure consistency across reporting systems - Identify gaps in processes and recommend data-backed improve .

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