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BrusselsHybridHours negotiable<3months
You will offer your services as an expert, based in an urban environment in Belgium.
- The work will be carried out either at the company's premises or on site at customer premises.
- You will be integrated into a Development team that will closely cooperate with a major client's IT team.
What you do
- Design and develop scalable, reliable data transformation pipelines and workflows.
- Optimise the performance and execution times of dbt projects.
- Design, implement, and document data quality checks and control tests using dbt capabilities.
- Establish automated regression testing and validation procedures to ensure pipeline output quality and consistency.
- Promote and share best practices for maintaining, managing, and evolving dbt projects across the team.
- Analyse business requirements and translate business analysis documentation into robust, operational data transformation pipelines.
What they ask
- University degree in Computer Science, Information Technology, Systems Engineering, or relevant discipline.
- Minimum 12 years of relevant working experience in IT.
- Hands-on experience with dbt, preferably dbt Core, for designing, maintaining, testing, documenting, and extending production-grade analytics transformation projects.
- Experience in analytics engineering and modern data modelling, including layered architectures, reusable macros, data quality controls, and CI/CD practices.
- Experience developing PySpark transformation scripts for large-scale data processing; Spark job optimisation would be an asset.
- Hands-on experience with Apache Iceberg, including partition and schema evolution, snapshot management, and time travel.
- Experience with job orchestration and scheduling tools, such as Dagster, for managing dbt and Spark workloads.
- Strong knowledge of SAS-based ETL and legacy codebases; experience migrating SAS workflows to dbt is a strong asset.
- Excellent SQL skills, including complex transformations, performance optimisation, and incremental processing.
- Good understanding of Apache Spark execution, partitioning strategies, file layout optimisation, and performance tuning.
- Strong knowledge of data validation, reconciliation, and testing to ensure functional equivalence when migrating legacy systems to dbt and PySpark.
- Knowledge of version control and collaborative development practices, including Git, code reviews, release management, and automated deployment pipelines.
- Excellent command of the English language.
How to apply
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