Senior Full-Stack Engineer
Pubblicato il 13-08-2026 - Altro in Italia
What You Will Do
Own and evolve data pipelines that power Klarna's externally-facing dashboards used by hundreds of thousands of merchants worldwide.
Participate in all Partner Reporting initiatives, optimizing data flows to improve scalability, reliability, and standardization across markets and merchant segments.
Build and maintain ETL / ELT pipelines that transform raw Klarna event and transaction data into merchant-ready KPIs such as funnel metrics, and customer insights.
Automate and simplify complex reporting, replacing manual or semi-automated workflows with robust, production-grade data products.
Collaborate closely with Data Modeling, Product Analytics, and Product Engineering teams to streamline pipelines, remove unnecessary transformation layers, and improve end-to-end data efficiency.
Apply strong SQL-based analysis and feature engineering to ensure metrics are correct, explainable, and trusted — internally and externally.
Help shape the foundation of a merchant analytics platform that goes beyond reporting: uncovering consumer trends, identifying high-intent shoppers,
measuring campaign effectiveness, and enabling data-driven recommendations for partners.
Tech Stack
Python (must-have)
SQL (must-have; complex analytical queries at scale)
REACT (nice-to-have; basic front-end development)
Airflow (or equivalent orchestration tools)
Batch and ELT-style pipeline patterns
Experience with workflow schedulers, jobs, and dependency management
AWS (S3, Redshift, IAM, etc.)
Redshift (or similar analytical data warehouses)
Jenkins for CI/CD and pipeline automation
Git-based workflows and production deployment best practices
Who You Are
Proven experience as a Data Engineer building and operating production-grade data pipelines.
Strong ownership mindset over mission-critical data products, primarily batch-based, with an appreciation for scalability and reliability.
Deep comfort with data modeling, schema evolution, metric definitions, and data quality in analytical e