Address
Form of workThe FinTech Accounts Receivable (AR) Cash Applications (Cash Apps) team handles 12M+ receipts ($161B+) per year, with 24% YoY growth across 100+ channels. The top goals are timely and accurate identification of customers, invoices, and application of payments to open balances to improve Amazon's Days Sales Outstanding (DSO), free cashflow and customer credit availability.
Cash Apps uses OCR, NLP, and ML to automate processing, eliminate errors, and generate recommendations, improving productivity. A Sr. SDE is needed to influence architecture across services, solve complex problems, drive initiatives through cross-org collaboration, and lead resilient and highly available designs to achieve 96% automated processing of projected 34MM receipts by 2025.
Key job responsibilities
The Sr. SDE is a technical leader who ensures that security and scalability are at the core to their team's designs. They Implement new software by designing, coding and launching multi-tenant cash application services.
Sr. SDE defines bar raising standards for operational excellence and engineering best practices. They lead by example in implementing the standards and educate others by coding critical parts of the software, provide insightful code reviews, lead design reviews, review internal and external designs, and influence other teams. They oversee the development progress to identify the technical blockers or process bottlenecks, and proactively take lead in resolving them. They act as a force-multiplier by engaging with SDEs and provide needed mentoring and coaching support.
About the team
FinTech Accounts Receivable (AR) Cash Applications team is responsible for ingesting cash received in Amazon bank accounts into AR sub ledgers, accurately identifying customers and invoices for each payment, and applying payments to customers' open balances. Cash Applications services use mechanisms like analyzing remittance information, parsing customer email correspondence, and Machine Learning models to automate cash application to reduce human errors and to improve analysts' efficiency by generating recommendations where automation is not possible.
We are open to hiring candidates to work out of one of the following locations:
Jersey City, NJ, USA
