Data Engineer

About this role

Responsibilities

• As a Data Engineer, you’ll be building the data platforms that everything else depends on. From raw source systems to production-ready models consumed by analysts, ML pipelines, and business users. You own the path data takes to become useful. The work is hands-on Python and SQL, heavy on PySpark, and grounded in Kimball modeling and lakehouse architecture.
• You come in before the solution is defined. Together with the client, you shape what gets built, then you build it. You'll work in cross-functional delivery teams alongside platform engineers and AI developers, each owning a distinct layer of the stack.
• A few examples of what this looks like in practice:
• Building a Kafka-based ingestion framework for a major insurance group, onboarding several thousand tables at scale so multiple downstream teams can actually use the data they need.
• Lifting an on-prem data warehouse into a cloud-based Databricks lakehouse for a Nordic logistics company, standing up CI/CD, integration, modeling, and quality assurance, then scaling for new use cases.
• Auditing a manufacturing company's data platform against best practices, delivering a migration plan, then staying on to bring in new sources, optimize workflows, and build out analytics models.
• The bar for what "done" means here is high: production-grade, observable, secure and used.
• What you get
• Assignments that accelerate you. Every 6–18 months you're in a new engagement. New industry, new architecture decisions, new stakeholders to