Delighting users while fairly compensating artists and creators is at the core of Spotify’s mission. To accomplish this we are constantly striving to more efficiently monetize our products. Spotify seeks engineers to focus on growing one of the world’s largest and fastest growing digital subscription businesses while mitigating risk and making sure that we are at the forefront of minimizing fraudulent behavior on our payments platform.
We have ambitious plans in the Premium team, and that comes with the knowledge that we need to understand the payments space which allows us to monetize our vision and minimize fraud. Do you have experience with ingesting, analyzing and processing massive amounts of data for use in real-time systems? Do you have a passion for understanding the complex nature of taking payment at scale from a merchant perspective? If so then we want you to join the band!
What you’ll do
- Developing the technical fraud and risk strategy for our payments platform.
- Help grow Spotify Premium by creating the platform and tools needed to prevent fraud and mitigate risk and fraud from a merchant perspective, in a complex global payments landscape.
- Collaboratively define and drive technical strategy and roadmap. Evangelizing to the product teams, operators, and broader organisation.
- Work closely with operations and compliance teams to make sure that you collaboratively provide for the company’s short- and long-term goals, differentiating between building what is asked for and what is truly needed in the risk domain.
- Develop systems to gather signals from payment and identity data providers, to distinguish fraudulent activity from legitimate
- Be a part of the seed of a new internal function to develop our risk engineering strategy.
Who you are
- Minimum of 3 years experience implementing large-scale data-driven production systems
- Minimum 2 years of experience in fraud-detection and prevention systems, methodologies from a merchant perspective.
- Experience in working with data-driven applications at scale
- Excellent communication skills, written and verbal
- Data analytics frameworks such as Kafka, Storm, Spark, Akka
- Knowledge of Hadoop and Map Reduce
- Knowledge of Scala, Java, Go or Python