Who we are?

There’s a fintech revolution underway in Mexico, and Clip is leading the way!

Clip is the leading platform for digital payments and financial solutions. We were born with the idea that all businesses in Mexico should have the opportunity to access the latest innovation in financial technology, driven by #ElPoderDeClip.

Being part of Clip will make you proud. You will work in what you are passionate about within a talented community, in a safe space where you can be your best self and where growth means constant learning.

The Role

We are looking for a Fraud Prevention Engineering Senior Manager to join our fast-growing fintech and lead a high-performing team focused on delivering robust data products and transactional applications. This role involves deploying machine learning models into production, enhancing software engineering practices, and ensuring systems meet the stringent requirements of low-latency, high-availability transactional environments.

The ideal candidate will collaborate with cross-functional teams to align engineering efforts with business goals, foster a culture of technical excellence, and ensure seamless integration of data and engineering solutions. They will manage risk, maintain compliance, and drive innovation to build scalable and reliable systems that support the company’s mission in fraud prevention and financial services

What will I be doing?

1. Stakeholder Management: Collaborate with product, fraud prevention, data science, and compliance teams to understand requirements, translate them into engineering solutions, and ensure alignment with business objectives. Act as a bridge between technical and non-technical teams.
2. Team Leadership and Development: Lead and mentor a team of engineers, fostering a culture of technical excellence, collaboration, and continuous learning. Align team goals with organizational priorities, while empowering members to innovate and take ownership of their work.
3. Machine Learning Deployment and Monitoring: Oversee the deployment of machine learning models into production systems, ensuring they meet performance, scalability, and reliability standards. Establish monitoring processes to track model health, accuracy, and performance over time.
4. Software Engineering Excellence: Drive the adoption of best practices in software engineering, including clean code, robust testing frameworks, and continuous integration/continuous deployment (CI/CD) pipelines, to ensure reliable and maintainable systems.
5. Transactional Application Development: Lead the development of low-latency, high-availability transactional applications that meet the company’s stringent performance and scalability requirements.
6. Fraud Prevention Engineering Strategy: Partner with leadership to co-develop and execute a technical strategy that supports fraud prevention goals, providing guidance on architecture, tools, and practices to the engineering team.
7. Infrastructure and Architecture: Design and maintain scalable, efficient, and cost-effective infrastructure to support transactional systems, machine learning pipelines, and real-time fraud detection applications.
   

Programming and Scripting: (Mandatory)

1. • Proficiency in Java and/or Kotlin for building and maintaining scalable applications.
   • Experience with Python, particularly for scripting, automation, and machine learning workflows.

Software Engineering Practices: (Mandatory)

1. • Expertise in designing and implementing low-latency, high-availability transactional applications.
   • Strong understanding of CI/CD pipelines and testing strategies for reliable deployments.
   • Familiarity with transitioning away from event-driven architectures (e.g., Lambdas and queues) to more scalable solutions.

Software Development Lifecycle: (Mandatory)

1. • Strong understanding of Agile, DevOps, and modern software development practices tailored to engineering and data-centric applications.

Data Infrastructure and Management: (Mandatory)

1. • Understanding of AWS services such as Kinesis, S3, and DynamoDB for building robust data pipelines and storage solutions.
   • Familiarity with Databricks/Spark for big data processing and analytics.
   • Knowledge of streaming technologies (e.g., Apache Kafka or AWS Kinesis) for real-time data processing.
   
   

Clip was born with the genuine idea of financial inclusion and this has been our way of living ever since.

In Clip, we are committed to a diverse and inclusive workplace. Clip does not discriminate on any basis of origin, gender identity, sexual orientation, race, disability, age or other legal status. Clip is an equal opportunity employer.

If you are unstoppable, creative and have the skills we need, we want to hear from you!

Consult our privacy notice: https://www.clip.mx/privacidad.