Backend Software Engineer with 4 years of experience building critical infrastructure in the Fintech space. I specialize in designing scalable systems for payment processing, end-to-end data pipeline architecture (generation & ingestion), and third-party API integrations.
Payment Processor Architecture: Played a key role in Finix’s evolution into a Payment Processor by building the settlement handling ecosystem. Designed and implemented services to generate compliant settlement files and manage their ingestion by downstream systems, ensuring data integrity across the financial ledger.
Third-Party API Integrations (Disputes): Spearheaded the technical integration with top-tier card networks to automate chargeback handling.
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Navigated complex certification requirements and successfully mapped API responses with sparse documentation.
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Built the logic to handle automated dispute creation and status updates.
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Built PDF generation service for submission of files to card networks.
Document Handling Microservices: Implemented a normalization engine for dispute files. The system validates and reformats customer-uploaded documents to meet strict card network specifications regarding file size and structure.
Test Automation Leadership: Championed software quality by writing and maintaining a comprehensive suite of End-to-End JBehave tests. This initiative drastically reduced regression bugs and was recognized as a benchmark for testing maturity compared to other engineering verticals.
Supported the upgrade process for the monitoring infrastructure at NERSC by pre-processing recently migrated data, redirecting data streams to visualization platforms and creating new dashboards for use in operations. Investigating methods to identify data loss as well as knowledge discovery for the data.
C++ and Python. I held classes twice weekly as well as graded homework assignments. At office hours I worked with students to understand concepts. I was nominated for the Outstanding Teaching Assistant Award 2020-2021.
Developed an Android application. The goal was to help with the COVID-19 situation by connecting users with healthcare professionals, providing verified information and give greater freedom for individual actions. Features included manual and automated scans for other devices in close proximity as well as self-assessments for symptoms, and a tool to track the self-quarantine period. This was funded by the FSU Collaborative Collision "Achieving economic normality and public health via deep learning modeling and contact tracing."
I worked on Data Collection for the Operations Monitoring and Notification Infrastructure (OMNI), in regards to temperature and humidity sensors, as well as power usage. My project was to restore functionality to devices so that data is collected for further analysis. I documented the troubleshooting process so that resolving issues is easier in the future.
