Lead data engineer
Pubblicato il 31-08-2026 - PLP Group in Medolago
Overview Build and operate Klarna's analytical data processing platforms on AWS, supporting large-scale batch and analytical workloads. Design robust data pipelines, table structures, and access patterns enabling efficient analytics, model training, and downstream consumption. Continuously evolve platform architecture, infrastructure, and AWS account foundations to support long-term scale, cost efficiency, security, and reliability.
Si ricordi di controllare il suo CV prima di candidarsi. Inoltre, si assicuri di leggere tutti i requisiti relativi a questo ruolo.
Lead the design and implementation of core data processing systems using technologies such as AWS Glue, Spark, S3, Iceberg, Databricks, and Redshift. Set a high bar for code quality, maintainability, and operational excellence, acting as a role model for best practices in the domain. Drive architectural decisions with a holistic view of Klarna's data landscape, downstream consumers, and cross-team dependencies.
Design and evolve IAM models, roles, and access patterns to enable secure and scalable use of data platforms across teams and accounts.
Identify and solve complex problems related to performance, scalability, cost, security, and compliance. Act as a technical leader in cross-team initiatives, facilitating discussions, aligning stakeholders, and delivering production-ready solutions.
Qualifications
Strong experience building analytical data platforms on AWS, including Spark-based processing and S3-backed data lakes. Solid hands-on skills with Python, distributed data processing, and infrastructure-as-code (Terraform).
Experience working with modern table formats (e.g. Iceberg) and data warehouses (e.g. Redshift).
Experience designing and operating IAM and AWS account setups for data platforms, balancing security with developer usability. A product-oriented mindset, focused on building durable, scalable solutions rather than one-off pipelines. xkiyazw Experience operating systems in production and improving them through monitoring, incident learnings, and iteration.