Dbt Analytics Data engineer

ThoughtLabs Belgium; Brussel Hoofdstad; Hybrid; EUR 70,000 - 110,000; Full time

Job summary

ThoughtLabs Belgium is seeking an experienced Analytics Engineer to design and maintain scalable data transformation pipelines using dbt, PySpark and Spark, with a focus on performance.

You will implement data quality controls, set up automated regression tests, document transformation logic and collaborate with data engineers, analysts and stakeholders to translate business requirements into reliable BI-ready datasets. Brussels-based with potential hybrid work.

Qualifications

• Experience designing, maintaining and extending modular analytics transformation projects in production environments.
• Strong data modelling across layered architectures (staging, intermediate, marts).
• Proficient in SQL for complex transformations and performance optimisation.
• Experience with PySpark for large-scale data processing and Spark optimisation.
• Familiarity with version control, code reviews, and automated deployment pipelines.

Responsibilities

• Design and maintain transformation pipelines for scalable data workflows.
• Optimise execution times of dbt projects.
• Design, implement and document data quality checks and control tests.
• Set up automatic regression tests and validation procedures of pipeline outputs.
• Introduce and share best practices for managing dbt projects.
• Translate business analysis documents into operational data pipelines.

Skills

Analytics engineering

SQL

PySpark

Spark tuning

Data quality controls

Documentation

Team collaboration

Communication with stakeholders

Cloud data platforms

Education

EQF Level 6+ in Computer Science / IT / Systems Engineering

Tools

dbt

Apache Spark

Dagster

Git

SAS

Apache Iceberg

CI/CD

Job description

• Design and development of transformation pipelines for scalable and reliable data workflows
• Optimisation of execution times of dbt projects
• Design, implement and document data quality checks and control tests using the functionalities provided within the dbt landscape
• Set up automatic regression tests and validation procedures of pipeline outputs
• Introduce and share best practices within the team about managing and evolving dbt projects
• Interpret business analysis documents and translate them into operational transformation pipelines

Specific expertise and technologies

• Proven hands-on expertise with dbt (preferably core) for designing, maintaining, testing, documenting and extending modular analytics transformation projects in production environments.
• Strong experience in analytics engineering and modern data modelling, including layered architectures (for example staging, intermediate and marts), reusable macros, data quality controls and CI/CD practices for dbt projects.
• Strong proficiency in SQL for complex transformations, performance optimisation and incremental processing.
• Practical experience in developing PySpark transformation scripts for large-scale data processing. Familiarity with optimisation of Spark jobs is a plus.
• Good understanding of Apache Spark execution concepts, partitioning strategies, file layout optimisation and performance tuning in distributed data environments.
• Proven experience working with Apache Iceberg tables, including partition evolution, schema evolution, snapshot management and time travel. Ability to work with the internals of the technology, such compaction strategies, transaction behaviour and implications for performance is considered a plus.
• Strong knowledge of data validation, reconciliation and testing approaches to ensure functional equivalence, especially when migrating from legacy proprietary software to dbt and PySpark-based implementations.
• Familiarity with version control and collaborative development practices, especially Git-based workflows, code review, release management and automated deployment pipelines.
• Experience with job orchestration and scheduling tools (e.g. Apache Spark, Dagster) used to run dbt and Spark workloads in enterprise environments.
• Ability to produce clear technical documentation covering transformation logic, migration decisions, table behaviour, operational procedures and support handover.
• Capacity to collaborate with data engineers, analysts, business stakeholders and platform teams to translate legacy logic into scalable, well-governed modern data transformations.
• Solid knowledge of SAS-based ETL and capacity to understand legacy codebases, along with demonstrated ability to migrate legacy processing workflows from SAS to dbt, would be considered a strong plus.

Education

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