Database Analytics Engineer

EUROPEAN DYNAMICS

12d • Onsite

About The Position

The Database Analytics Engineer will provide expert services from Brussels, Belgium, either at the company’s premises or at customer premises. For the initial assignment, the successful candidate will join the company’s Development team and work closely with a major client’s on-site IT team. The role focuses on designing and developing scalable data transformation pipelines, improving dbt project performance, implementing data quality and regression testing, and translating business requirements into operational data pipelines. The position involves modern analytics engineering and data modelling, PySpark, Apache Iceberg, orchestration tools, SQL, and supporting the migration of legacy SAS workflows to dbt and PySpark.

Requirements

• University degree in Computer Science, Information Technology, Systems Engineering, or a relevant discipline, combined with a minimum of 12 years of relevant IT work experience.
• 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.
• 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.
• 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.

Nice To Haves

• Spark job optimisation experience is an asset.
• Experience migrating SAS workflows to dbt is a strong asset.

Responsibilities

• 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.

Benefits

• Competitive remuneration, either on a contract basis or with a full benefits package, based on qualifications and experience.
• Opportunity to work as part of a team serving a major European Institution in an international, multilingual, and multicultural environment, with opportunities to make a difference.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior