Annually, Amazon makes $150B worth of payments to more than two millions supplier and content owners around the globe. Efficiency in purchasing, receiving, invoicing and payments (P2P) processes is the key to accurate and timely payments. Unnecessary supplier hold, inefficient invoice submission methods, manual errors, incorrect bank account information or short-paying supplier, all of these lead to delayed payments that directly impacts supplier trust. P2P analytics team within FinTech BI builds financial models to provide insight into metrics to measure P2P efficiency, highlight defects, identify root causes and enable accurate forecasting capabilities.
We are looking for a BI Manager to lead a team of BI and Data Engineers. This person will lead the team in building this large-scale near real time data ingestion, calculation engine, and a business facing analytical tool for account payable teams across all Amazon business lines. He or she will work across Amazon engineering teams and business teams, such as Finance and Vendor experience teams across the globe, in planning, designing, executing and implementing this analytical platform.
Play a lead role in the architecture, design, implementation and deployment of extremely large scale, critical, and complex finance application, taking into account the long term impact and performance considerations. Architect highly efficient data and reporting structures, making a tradeoff between scalability, performance and user functionality needs, using expert knowledge in software development technologies. Independently gather business and functional requirements and translate these requirements into robust, scalable, operable solutions that work well within the overall data architecture. Architect platform and application components based on the evaluation of alternate solutions. Develop long term domain/technology strategies and significantly influence the process and standards associated with project engineering. Lead design reviews for other Software Development Engineers and offer feedback on design, integration, performance and scalability issues. Serve as a technical lead for medium and large projects and delegate assignments to team or cross functional participants. Serve as an authority in the area of technical and domain expertise and mentor, develop, and train junior data engineers.