What you'll do
• Own and evolve the global feature engineering platform that powers Klarna's risk decisions and models
• Design and improve feature definitions with stakeholders to improve predictive power and accuracy of risk assessments
• Build and architect data pipelines to solve complex problems in ingestion, transformation, and real-time serving—and support what you build
• Instrument quality and observability: alerts, dashboards, canary releases, incident follow-up
• Design systems and solutions that integrate with Klarna's ecosystem of AI tooling

Tech stack

• Languages: Python, SQL

• Frameworks & data: Spark (PySpark), Kafka, open table formats (Iceberg, Delta Lake), Redis

• Cloud: AWS, DynamoDB, Databricks

• DevOps: Terraform, Docker, CI/CD

Who you are
• Ownership-minded over mission-critical data pipelines and serving (batch + streaming + API); you maintain production systems while shipping new features
• Tech-stack flexible: you adapt to the problem, are comfortable across Python, Spark and Kafka, however also curious to explore modern tools and frameworks to challenge the status quo
• Strong on data modeling and understanding business requirements and data relationships—you translate use cases into robust pipelines and feature definitions
• You ship in time-boxed phases, scope pragmatically, and drive projects to delivery—including rescoping or stopping work that isn't delivering value
• You communicate clearly and collaborate well with stakeholders and peers; you unblock yourself and others when priorities shift
• High tempo and autonomous: you're passionate about owning products and solutions and engaged in leading projects
• Curious to explore AI-assisted development and tooling (e.g. LLMs, Copilot, Cursor) as part of your workflow

Please include a CV in English.

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