Senior dbt Analytics Engineer – PySpark
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We are looking for a Senior Analytics Engineer in Brussels, Belgium , with strong expertise in dbt, SQL, PySpark and Apache Iceberg .
You will design and optimize scalable data transformation pipelines, modernize legacy data processing workflows and ensure data quality across enterprise analytics environments.
Hybrid Brussels – 6+ years of IT experience
Tasks and Responsibilities
- Design, develop and maintain scalable data transformation pipelines using dbt and PySpark;
- Optimize dbt project performance , SQL transformations and incremental data processing;
- Develop and maintain data quality checks, automated regression tests and pipeline validation procedures ;
- Design modular dbt models using staging, intermediate and marts layers, reusable macros and established analytics engineering practices;
- Work with Apache Iceberg tables , including schema evolution, partition evolution and snapshot management;
- Translate business requirements and legacy processing logic into reliable, production-ready data transformations;
- Support migration and reconciliation activities, ensuring functional equivalence between legacy and modern data pipelines;
- Establish dbt development best practices, maintain technical documentation and collaborate with data engineering and business teams.
Profile
- Bachelor's or Master's degree and 6+ years of IT experience ;
- Proven hands-on experience with dbt , preferably dbt Core, including production development, testing, documentation and maintenance;
- Strong knowledge of analytics engineering, modern data modelling and modular dbt architectures ;
- Advanced SQL skills , including complex transformations, query optimization and incremental processing;
- Practical experience with PySpark and Apache Spark , including distributed processing, partitioning and performance optimization;
- Proven experience with Apache Iceberg , including schema evolution, partition evolution, snapshot management and time travel;
- Strong understanding of data quality, reconciliation, automated testing and validation , particularly during legacy system migrations;
- Experience with Git, CI/CD pipelines, code reviews and automated deployment practices ;
- Familiarity with workflow orchestration and scheduling tools , such as Dagster, for dbt and Spark workloads;
- Ability to document technical solutions and collaborate with data engineers, analysts, business stakeholders and platform teams.
Experience with SAS-based ETL and migration from SAS to dbt would be a strong advantage. Knowledge of Spark job optimization and advanced Apache Iceberg internals, including compaction and transaction behavior, would also be beneficial.
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