Lead Engineer/Data Engineer
Pubblicato il 26-09-2026 - Klarna in Italia
Overview In this role you will build and operate Klarna's analytical data processing platforms on AWS, enabling large-scale batch and analytical workloads. You will shape data pipelines, table structures, and access patterns to support analytics, model training, and downstream consumption. You'll evolve platform architecture for scale, cost efficiency, security, and reliability, while leading cross-team data initiatives and upholding high code quality and operational excellence.
Responsabilità
Build and operate analytical data processing platforms on AWS to support batch and analytical workloads
Design robust data pipelines, table structures, and access patterns for analytics, model training, and downstream consumption
Evolve platform architecture and AWS foundations for scale, cost efficiency, security, and reliability
Lead core data processing system design using AWS Glue, Spark, S3, Iceberg, Databricks, and Redshift
Set high standards for code quality, maintainability, and production readiness
Drive architectural decisions with awareness of the broader data landscape and cross-team dependencies
Design and evolve IAM models, roles, and access patterns for secure, scalable data platform use
Identify and resolve complex performance, scalability, cost, security, and compliance challenges
Act as a technical leader in cross-team initiatives, delivering production-ready solutions
Requisiti fondamentali
Extensive experience building analytical data platforms on AWS with Spark processing and S3-backed data lakes
Strong Python skills, distributed data processing, and infrastructure-as-code (Terraform) experience
Experience with modern table formats (Iceberg) and data warehouses (Redshift)
Experience designing and operating IAM and AWS account setups for data platforms
Product-oriented mindset focused on durable, scalable solutions rather than one-off pipelines
Experience operating systems in production with monitoring, incident handling, and iteration
Cross-functional coll