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.
Candidates need to be based in Brussels or willing to relocate to Brussels.
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 Masters 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 optimisation;
- 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.
Key Technologies & Skills
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