Requirements

7+ years of experience with Java (11+, 21+), with emphasis on depth and relevance of experience
Familiarity with Python
Experience with batch or stream processing (preferably Apache Spark and Flink/Kafka streams)
Familiarity with SQL and no-SQL (document) databases
Experience with complex systems distributed across several microservices, Kafka, scattered data and ownership
Hands-on knowledge of system integration patterns
Strong product mindset and passion for user experience, prioritizing customer-focused and data-driven decisions
Strong problem-solving skills to refine problem statements and solve them from first principles
High level of ownership including identifying impactful problems, stakeholder buy-in, and end-to-end implementation

Nice to Haves

ML Engineering background, such as model training pipelines, automated label quality assurance, and model experimentation frameworks
Data Engineering background, including experience with large-scale data pipelines, feature engineering, serving infrastructure, graph algorithms or embedding techniques, and data governance practices
Experience with Apache Iceberg and/or Trino

Working at the intersection of Backend Engineering, data, and machine learning to solve complex engineering problems
Providing guidance for Wise's data engineering and machine learning community
Taking ownership of difficult technical problems
Building tools and automation that help FinCrime teams develop and improve machine learning models
Removing repetitive engineering work to enable teams to focus on identifying and preventing financial crime effectively at scale
Onboarding and understanding team and tribe vision to contribute effectively
Applying company values to guide your work
Understanding causes behind problematic payments and customer issues to develop solutions
Building financial crime prevention systems to protect customers
Helping to scale up and build a world-class money transfer product by solving technical challenges