Senior Backend Developer

Fraud.net is looking for a Senior backend developer.

Fraud.net is an emerging technology company supporting some of the world’s most prominent companies across multiple continents. We are seeking an experienced Senior Backend Developer to    join our growing team. 

We are committed to building easy-to-use, innovative tools to empower all businesses to prevent fraud. We are an experienced team with diverse backgrounds in analytics, corporate security, data science, engineering, and e-commerce.

We are looking for someone who can help advance the company’s technology, who can contribute to where we take the product and how we build out the team. You'll be working with cutting edge technologies including machine learning, Node.js, TypeScript, AWS Lambda, ElasticSearch, DynamoDB and more. 

Fraud.net is a leader in AI-powered enterprise risk intelligence. Its award-winning fraud detection platform helps digital businesses to quickly identify transactional anomalies and pinpoint fraud using artificial intelligence, big data and live-streaming visualizations. Fraud.net’s platform was designed to combat hard-to-detect fraud at digital enterprises in the e-commerce, travel and financial services sectors. Its unified algorithmic architecture combines: 1) cognitive computing/deep learning, 2) collective intelligence,  3) rules-based decision engines, and 4) streaming analytics to detect fraud in real-time, at scale.

Job Description

We are looking for a backend developer to join our small but growing team. Your primary focus will be developing highly performant and scalable APIs for the front-end. You will also be responsible for integrating with the front-end and QA teams to deliver production-ready features. Therefore, a basic understanding of Angular is necessary as well. The backend developer will also provide support for the DevOps and Data Science teams. 

Responsibilities

• 5+ years experienced in Software Development. Excellent team player with good analytical, strategic planning and interpersonal and communication skills.
• Experience working with Agile Engineering Best Practices such as TDD
• Delivering testable and well-documented APIs in Open API format
• Writing reusable, stateless, and efficient code
• Design and implementation of low-latency, high-availability, and performing applications

Skills And Qualifications

• Experience with AWS Lambda and Kinesis streams 
• 3-5 years experience with Node.js and functional programming
• Provide strong technical leadership and guidance to TD development
• Experience writing efficient queries for NoSQL databases like DynamoDB and ElasticSearch
• Contribute to comprehensive documentation that supports the development and system support lifecycles
• Understanding the nature of asynchronous programming and its quirks and workarounds
• User authentication and authorization between multiple systems, servers, and environments
• Strong proficiency with TypeScript and/or modern JS
• Understanding fundamental design principles behind a scalable application
• Proficient understanding of code versioning tools (GIT)
• Strong work ethic with good time management with ability to work with diverse teams and lead meetings.

Disclaimer: 

The advertised pay scale reflects the good faith minimum and maximum salary range for this role. The advertised pay scale is not a promise of a particular wage for any specific employee. The specific compensation offered to a candidate may be dependent on a variety of factors including, but not limited to, the candidate's experience, education, special licensing or qualifications, and other factors.

Fraud.net is a leader in AI-powered enterprise risk intelligence. Its award-winning fraud detection platform helps digital businesses to quickly identify transactional anomalies and pinpoint fraud using artificial intelligence, big data and live-streaming visualizations. Fraud.net’s platform was designed to combat hard-to-detect fraud at digital enterprises in the e-commerce, travel and financial services sectors. Its unified algorithmic architecture combines: 1) cognitive computing/deep learning, 2) collective intelligence, 3) rules-based decision engines, and 4) streaming analytics to detect fraud in real-time, at scale.