Responsibilities:
- Develop, integrate, enhance, migrate, scale, optimize, and build fraud detection and data flow, pipelines, and processing applications and systems using Java 17, Spring Boot 2, Drools, Guava, Cassandra, Oracle DB, Kubernetes, Jenkins, Elasticsearch, Kibana, Prometheus, Grafana, Splunk, and Kafka.
- Develop Fraud Gateway that handles real-time (HTTP) and non-time-sensitive (Kafka) data flows for fraud analysis, ensuring data validation, enrichment, and transformation to align with machine learning model schemas.
- Build data flow pipelines for Spark Shopper teams with fraud detection engine Inkiru, enabling real-time audit support for store associates during Spark Shopper checkouts, improving efficiency and accuracy.
- Migrate NonPCI data flows to a separate NonPCI environment, improving performance and response time by reducing firewall overhead, and enhancing expandability by minimizing the extensive auditing in the PCI environment.
- Facilitate seamless integration between fraud detection models and cross-functional teams, specializing in fraud detection and data processing.
- Enhance application maintainability by extracting lower-level logic to a library (Hodor) and business logic to JSON, enabling easy-to-use mapping between upstream attributes and the fraud detection model schema through a UI.
- Utilize comprehensive technology stack including Java 17, Spring Boot 2, Drools for business rules, Guava for caching, Cassandra and Oracle for data storage, Kubernetes for orchestration, Jenkins for CI/CD, and monitoring tools including Elasticsearch, Kibana, Prometheus, Grafana, and Splunk to ensure efficient development, deployment, and monitoring of system.
- Scale PCI and NonPCI environments demonstrate reliability and responsiveness for critical fraud detection services.
- Optimize performance in legacy code by reducing costly serialization and deserialization of upstream JSON payloads during evaluation, validation, and enrichment, instead passing JSON objects directly within the code, boosting performance substantially.
- Establish data flow pipelines between customer contact risk systems (ChatBot and Customer Care) and fraud detection engines (Inkiru and Accertify), enhancing fraud detection by integrating ThreatMetrix (TMX) session behavior with customer account data.
Requirements:
- Require Master of Science in Computer Science/Engineering, Information Systems/Technology, or related field.
Worksite address:
Ryzlink Corp., 7500 Rialto Blvd., Bldg. 1, Suite 250, Austin, TX 78735
Please mail your resume to Attn: HR Manager, Ryzlink Corp DBA Chuwa America, 2858 Stevens Creek Blvd, Suite 207, San Jose, CA 95128