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Backend Engineer - Machine Learning and Data Engineering

1,280,000 - 1,700,000HUF/Monthly + RSUs
  1. Technology
  2. Budapest

Company Description

Wise is a global technology company, building the best way to move and manage the world’s money.
Min fees. Max ease. Full speed.

Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.

As part of our team, you will be helping us create an entirely new network for the world's money.
For everyone, everywhere.

More about our mission and what we offer.


Job Description

The team you will be joining: Servicing Machine Learning & Data Engineering 

The team's mission is to provide a reliable, efficient, and centralized platform for all data points used in risk assessments. We own the feature lifecycle tracks from finding, experimenting, and building data points to using them throughout risk control and handling use cases. We allow risk teams to build better performing controls by continuously improving labels, discovering new fincrime typologies or adopting new technologies. Our focus is on creating the necessary tools and procedures that support these activities.

Our main vision points are:

  • We provide high-quality, reliable data

  • We serve predictive features that capture unwanted (crime related) behavior

  • We maintain effective models that help us scale and adapt to change

  • Comprehensive controls that prevent threats and satisfy regulators.

 

As a Backend Engineer, your job will be to provide technical assistance and guidance to Wise's data engineering and machine learning community, taking ownership of difficult technical problems.

In order to succeed, you’ll need to be able to participate in the vision for data engineering through documents and conversations, get buy-in from stakeholders and leadership, communicate well in different levels of complexity and create and review pull requests.

What does it take?  

  • 5+ years of Java 11+ knowledge (and willingness to quickly learn python)

  • Experience with batch OR stream processing (preferably Apache Spark and Flink/Kafka streams)  

  • Experience with complex systems distributed across several microservices, kafka, scattered data and ownership

  • Willingness to contribute in UI-related projects

  • A strong product mindset and passion for user experience, you prioritise work with the customers in mind and  make 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

  • Ability to drive complex technical decisions and get buy-in from stakeholders, take ownership of difficult decisions, pushing back against bad ideas

  • Hands on knowledge of system integration patterns 

What does success look like?

  • 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


Additional Information

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.

If you want to find out more about what it's like to work at Wise visit Wise.Jobs.

Keep up to date with life at Wise by following us on LinkedIn and Instagram.

  1. Budapest
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