Sr. Software Dev Engineer, Fintech Account Receivables Cash Apps

Fintech Accounts Receivable Sr. Sde

The FinTech Accounts Receivable (AR) Cash Applications team manages business-to-business customer payments, with significant year-over-year growth across Amazon Business channels. The primary 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 cash flow, and customer credit availability. Cash Apps uses OCR, NLP, ML, and GenAI 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.

Key Job Responsibilities:

• The Sr. SDE is a technical leader who ensures that security and scalability are at the core of 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, providing insightful code reviews, leading design reviews, reviewing internal and external designs, and influencing other teams.
• They oversee the development progress to identify technical blockers or process bottlenecks and proactively take lead in resolving them.
• They act as a force-multiplier by engaging with SDEs and providing needed mentoring and coaching support.

About the Team:

The 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.