Who we are
Klarna’s mission is to free people from all the meaningless time spent managing money and purchases, so they can do more of what they love. Every day at Klarna we help consumers, merchants, and partners to explore just how smoooth the modern purchase experience can be. Our position at the crossroads of payments, consumer financing, ecommerce and banking means we are uniquely positioned to do this. There is no label for what we do.
Klarna was born in Stockholm in 2005 and today has 2000 employees working across Europe and the US. We currently serve 60 million consumers, work together with 90,000 merchants and process more than a million payment transactions a day. We are growing at 40% year on year and our investors include Visa, Atomico, Sequoia Capital, Permira and Bestseller group/ Anders Holsch Povlsen. We have strong partnerships with some of the world’s leading brands, such as ASOS, IKEA, Adidas, Zara, Lufthansa and Spotify.
Klarna strives to become the world’s favourite way to buy.
You can contribute to reaching this goal!
We are looking to hire great people, who are passionate about using their talents to generate success. Data Engineering is no exception! We are currently looking to grow our Data Engineering capability to satisfy the company’s ever increasing need to understand, predict and make the right decisions on large, fast, structured, unstructured and growing amounts of data!
What you will do
As a data engineer you will work in a team that is end to end responsible for data services, where a data service spans from management dashboards, data warehousing, real time decisioning pipelines to frameworks for data quality measures. As a data engineer you will typically take the role of building and running data pipelines, data warehouse, data frameworks or different data presentation techniques.
Your stakeholders will be Data Scientists, Business Analysts, other Engineers building Insights and all Klarna employees that need data in their daily work. We have amazing teams that take care of and become experts at different parts of the domain where sharing and collaboration are key.
Some of the technologies and languages we use are Kafka, Hadoop, Spark, AWS Redshift, AWS Glue, Apache Airflow, MS SQL, Qlik Sense, Postgres, Java, Scala, different NoSQL DB:s- and we are always looking for the most efficient tools for the job.