Data Engineer

Engineering at Klarna is an inspired customer focused community dedicated to crafting solutions that redefine our industry. Working in small highly collaborative Agile teams you and your team will have a clear mission and ownership of an important outcome that supports Klarna and our customers.

At Klarna we optimize for quality flow fast feedback focusing on endtoend ownership continuous improvement testing monitoring and experimentation. We aim for teams that are inclusive helpful and have a strong sense of ownership for the things they build. Our engineers make some of the most significant decisions for the company and we are looking for bold open and curious developers. As a Klarnaut youll be inspired to contribute to the growth of one of the Worlds most highly valued fintech and your work will impact the lives of our millions of users.

Were expanding several of our engineering teams including; teams working on our core checkout product payment services fraud prevention or improving our billing service and shipping credentials to name a few.

Help us make shopping online even more smoooth!

What youll get to do:

• Work with increasingly large volumes of data on resilient modern data infrastructure
• Own data products end to end
• Design and implement analytically suitable data structures from complex service data
• Advise upstream/downstream stakeholders on getting the best value out of our data always with security compliance efficiency and standardization at front of mind
• Be an integral part of a team in addition to its culture and ways of working. Common practices include agile methodologies pair and mob programming
• Succeed fail and learn together with other talented people.
• We believe in an environment that provides an opportunity for growth and see education as an outcome of failure that gets us closer to the next breakthrough

You should have:

• Strong working knowledge in data processing languages like SQL and Python Experience with Spark preferable
• Solid understanding of query and compute engines
• Experience in query design with consideration for platform
• Familiarity with dataset design practices and implementation strategies for multiple use cases (batch based analytics vs streaming)
• Understanding of common analytical practices & methods and proficiency in one or more languages used by analysts (Python R etc.)
• Strong business acumen and product oriented thinking
• Strong communication skills with emphasis on being able convey technical topics to differing levels of understanding and work closely on common problems with analyst and data science counterparts in English
• A burning curiosity to learn own relay and leverage the stories told by our data

Some of the technologies we work with:

• Data processing languages: SQL Python Pyspark dbt
• Analytical tooling such as Jupyter Notebooks.AWS (Redshift S3 Glue EMR)
• Data ingestion and streaming tools such as Kafka Flink Kinesis Firehose Apache Airflow