Software Engineer II – Fraud Prevention

Location: New York City, NY
Work Model: Hybrid – 3 days onsite per week
Contract: August 3, 2026 – July 30, 2027
Schedule: Full-time, 40 hours/week
Pay Rate: $65–$68/hour
Openings: 1

About the Company

Our client is a leading global financial technology company serving millions of customers worldwide through a portfolio of innovative financial and consumer technology products. The organization is committed to protecting customers, their financial information, and digital assets through advanced technology, data-driven solutions, and strong security practices.

About the Role

We are looking for an experienced Software Engineer II to join a high-performing Fraud Prevention team. This contractor will contribute to the design, development, and enhancement of scalable systems that help detect and prevent fraudulent activity while maintaining a seamless customer experience.

This is a highly hands-on engineering role, with approximately 80% of the work focused on software development, including writing new code, enhancing existing applications, and building automated tests.

You will work across the full fraud-prevention lifecycle, including analysis, detection, prevention, metrics, dashboards, and development of fraud-prevention capabilities.

Key Responsibilities

• Understand fraud use cases, policies, methodologies, and end-to-end prevention strategies.

• Develop, enhance, and maintain scalable, performant, and reliable software solutions.

• Write new code, modify existing code, and contribute to automated test development.

• Work confidently within existing and unfamiliar codebases.

• Contribute to fraud-prevention capabilities across distributed systems and services.

• Understand and work with other teams' systems, codebases, and CI/CD processes.

• Build and integrate microservices, databases, APIs, and event-driven solutions.

• Participate in technical design, requirements analysis, research, proof-of-concepts, and solution evaluation.

• Contribute to cloud-native development and modernization of legacy systems.

• Participate in code reviews and provide constructive technical feedback.

• Collaborate with product managers, analysts, data engineers, data scientists, risk teams, and other cross-functional stakeholders.

• Clearly communicate technical information, project status, risks, and blockers.

• Operate independently in an Agile environment while maintaining strong collaboration with the broader engineering team.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

• 3+ years of professional software engineering experience.

• Experience developing microservices, databases, and/or event-driven systems.

• Strong ability to read, understand, and modify code within an existing codebase.

• Experience with at least one high-level application framework or language such as:

  • Java / Spring Boot

  • Python

  • Golang

• Experience working with Agile software development practices.

• Strong problem-solving skills with the ability to independently research and identify solutions.

• Self-motivated and comfortable working with a high degree of autonomy.

• Strong communication and collaboration skills.

Preferred / Bonus Skills

• JavaScript experience.

• Experience with Kubernetes and Docker.

• Experience with AWS/cloud platforms.

• Experience with REST APIs.

• Experience with Drools or business rules engines.

• Knowledge or experience in the Fraud / Fraud Prevention domain.

• Experience working with Big Data systems.

• Experience with CI/CD and modern cloud-native development practices.

• A collaborative personality and good sense of humor.

Technical Skills

Software Development: Full-stack/backend development, feature development, bug fixes, proof-of-concepts
Cloud & Modernization: Cloud-native development, cloud architecture, legacy-to-cloud migration
Integration: Microservices, REST APIs, distributed systems, event-driven architecture
DevOps: CI/CD, Docker, Kubernetes, AWS
Testing: Automated testing and software quality practices
Engineering Practices: SDLC, Agile development, code reviews, technical specifications
Data: Databases and Big Data technologies
Fraud Technology: Fraud detection, prevention, risk analysis, rules-based systems

What Success Looks Like

The ideal candidate is a hands-on, self-starting engineer who can quickly understand an unfamiliar codebase, independently find answers, and deliver reliable technical solutions. You should be comfortable balancing strong technical opinions with collaboration and be able to communicate clearly with both technical and non-technical stakeholders.

Most importantly, you should enjoy solving complex problems and building technology that helps protect customers and prevent fraud at scale.