About the job
Feedzai is pioneering the world’s first RiskOps platform dedicated to financial risk management. As the market leader, we safeguard global commerce through an advanced cloud-based risk management system driven by machine learning and artificial intelligence. Our commitment is to secure the transition to a cashless society while fostering digital trust in every transaction and payment type. Renowned financial institutions, processors, and retailers globally rely on Feedzai to protect trillions of dollars, effectively manage risk, and enhance customer experiences without compromising privacy. As a Series D company, we have successfully raised $282M, achieving a valuation of $2 billion. Our cutting-edge technology protects one billion consumers and oversees 90 billion transactions annually.
The Risk & AI team within Customer Success is deeply engaged with our clients, tackling technical and analytical challenges with a business-oriented mindset and a customer-centric approach. This team actively designs risk management strategies, develops models and rules, implements data pipelines, and provides client training to ensure client success. Collaboration is key as they work with various departments including research, product, and marketing, maintaining a global team spirit to deliver top-tier risk prevention solutions. Joining our team means being at the forefront of combating financial crime and protecting individuals from financial harm. Join Us!
Your Day-to-Day:
- Understand, profile, and assess the quality of raw data from diverse client sources.
- Design, build, test, and maintain scalable and robust data pipelines for ingestion, transformation, and enrichment.
- Implement automated data validation, cleaning, and quality assurance processes.
- Collaborate with data scientists to support feature engineering and data preparation workflows.
- Optimize performance and cost efficiency of data workflows across cloud and on-prem environments.
- Lead discussions with client stakeholders on data architecture, integration, and delivery strategies.
- Partner with internal teams (Product, Engineering, Research) to improve platform tooling and reusability.
- Mentor and guide junior engineers in best practices, architecture design, and coding standards.

