Backend Engineer - Feature Platform

The team you will be joining: Feature Platform

Wise moves billions every year for millions of people and businesses across many different countries and currencies; while bad actors constantly evolve: scams, impersonation, account takeover, money laundering, mule networks, and other forms of abuse emerge. At this scale, in order to stay ahead of evolving threats, our automated controls and machine learning models require high-quality, real-time insights. Success lies in a centralized approach that provides the necessary governance, speed, and data consistency to power these critical systems.

Wise’s centralized feature engineering platform for financial crime detection is the Feature Platform. Its goal is that every fraud signal, AML indicator, and risk feature flows through a single governed platform instead of each team maintaining siloed calculations. It owns the feature lifecycle tracks from finding, experimenting, and building data points to using them in millions of transactions daily.

How can you contribute?

• Engineering. Tackle complex technical challenges focused on scalability, reliability, and speed, maintaining a high-performance infrastructure that processes millions of events with low latency.

• Innovation. Collaborate with Data Scientists and Engineers to design and implement cutting-edge feature topologies, including graphs, support vectors, and time-series data to stay ahead of evolving financial threats.

• Engagement. Drive platform adoption by partnering with teams across Wise to centralize existing risk logic and expand our self-service capabilities, ensuring that all risk signals are robust, governed, and consistent.
  

What does it take?

• 3+ years of Java 11+ knowledge, fluent in Java 21+

• Familiarity with Python (or willingness to learn on the job)

• 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

• A strong product mindset and passion for user experience, you prioritise work with the customers in mind and drive data-driven decisions to fix customer pain-points

• Strong problem-solving skills to help refine problem statements and figure out how to solve them with the available data and from first principles

• High level of ownership, including identifying impactful problems to solve, getting buy-in from stakeholders and implementing the solution end-to-end
  

Nice to have:

• Data Engineering background, such as experience with large-scale data pipelines, feature engineering and serving infrastructure, familiarity with graph algorithms and/or embedding techniques, awareness of data governance practices

• Experience with Apache Iceberg and/or Trino

• Experience with graph databases like Amazon Neptune
  

What does success look like?

• You'll be having a real world impact by building financial crime prevention systems which protect customers

• You’ll have onboarded and found your place through understanding your team and tribe vision and how you can contribute

• You’ll understand how our values can help you guide your work

• You’ll understand the reasons behind problematic payments and customer difficulties and how to go about solving them

• Understand our customers and the impact our product makes in their lives

• You’ll help us scale-up and build a world class money transfer product by finding solutions to our technical challenges and opportunities

For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.

We're proud to have a truly international team, and we celebrate our differences.
Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.

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