Dbt Analytics Engineer

You will have the opportunity to work in an international and multicultural environment on interesting European projects for a European organization.

What you will do.

• Design and develop transformation pipelines for scalable and reliable data workflows.
• Optimize the execution time of dbt projects.
• Design, implement and document data quality checks and control tests using dbt functionalities.
• Set up automated regression tests and validation procedures for pipeline outputs.
• Introduce and share best practices for managing and evolving dbt projects.
• Interpret business analysis documents and translate them into operational transformation pipelines.

What they ask Technical skills.

• Hands-on expertise with dbt, preferably dbt Core, in production environments. You can design, maintain, test, document and extend modular analytics transformation projects.
• Strong experience in analytics engineering and modern data modelling. This includes layered architectures such as staging, intermediate and marts, as well as reusable macros, data quality controls and CI/CD for dbt projects.
• Strong SQL skills for complex transformations, performance optimization and incremental processing.
• Practical experience developing PySpark transformation scripts for large-scale data processing. Spark job optimization is an advantage.
• Good understanding of Apache Spark execution, partitioning strategies, file layout optimization and performance tuning in distributed data environments.
• Proven experience with Apache Iceberg tables, including partition and schema evolution, snapshot management and time travel. Knowledge of compaction strategies, transaction behavior and performance implications is an advantage.
• Strong knowledge of data validation, reconciliation and testing. You can ensure functional equivalence when migrating from legacy proprietary software to dbt and PySpark.
• Familiarity with Git-based workflows, code reviews, release management and automated deployment pipelines.
• Experience with job orchestration and scheduling tools, such as Apache Spark and Dagster, for running dbt and Spark workloads in enterprise environments.
• Ability to produce clear technical documentation, covering transformation logic, migration decisions, table behavior, operational procedures and support handover.
• Ability to work with data engineers, analysts, business stakeholders and platform teams. You can translate legacy logic into scalable and well-governed modern data transformations.
• Knowledge of SAS-based ETL and the ability to understand legacy codebases are strong advantages. Experience migrating SAS workflows to dbt is also highly valued.

Education.

• EQF Level 6 or higher in Computer Science, Information Technology, Systems Engineering or another relevant field.