DBT Analytics Engineer

Nature of the tasks

• 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.

Skills

• Strong problem-solving and analytical skills.
• Strong organisational and time-management skills.
• Strong team spirit and experience working effectively within a collaborative team.
• Capability of integration in an international/multi-cultural environment.
• Excellent communication skills to articulate complex technical concepts.
• Attention to detail in software development and well-structured documentation.
• Ability to write clean, efficient, and maintainable code.