DBT Analytics Engineer

ul liDesign and development of transformation pipelines for scalable and reliable data workflows /li liOptimisation of execution times of dbt projects /li liDesign, implement and document data quality checks and control tests using the functionalities provided within the dbt landscape /li liSet up automatic regression tests and validation procedures of pipeline outputs /li liIntroduce and share best practices within the team about managing and evolving dbt projects /li liInterpret business analysis documents and translate them into operational transformation pipelines /li /ul pbSpecific expertise and technologies /b /p ul liProven hands-on expertise with dbt (preferably core) for designing, maintaining, testing, documenting and extending modular analytics transformation projects in production environments. /li liStrong 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. /li liStrong proficiency in SQL for complex transformations, performance optimisation and incremental processing. /li liPractical experience in developing PySpark transformation scripts for large-scale data processing. Familiarity with optimisation of Spark jobs is a plus. /li liGood understanding of Apache Spark execution concepts, partitioning strategies, file layout optimisation and performance tuning in distributed data environments. /li liProven 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. /li liStrong 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. /li liFamiliarity with version control and collaborative development practices, especially Git-based workflows, code review, release management and automated deployment pipelines. /li liExperience with job orchestration and scheduling tools (e.g. Apache Spark, Dagster) used to run dbt and Spark workloads in enterprise environments. /li liAbility to produce clear technical documentation covering transformation logic, migration decisions, table behaviour, operational procedures and support handover. /li liCapacity to collaborate with data engineers, analysts, business stakeholders and platform teams to translate legacy logic into scalable, well-governed modern data transformations. /li liSolid 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. /li /ul pbEducation /b /p ul liEQF Level 6 or higher in a relevant field such as Computer Science, Information Technology, or Systems Engineering. /li /ul pbSkills /b /p ul liStrong problem-solving and analytical skills. /li liStrong organisational and time-management skills. /li liStrong team spirit and experience working effectively within a collaborative team. /li liCapability of integration in an international/multi-cultural environment. /li liExcellent communication skills to articulate complex technical concepts. /li liAttention to detail in software development and well-structured documentation. /li liAbility to write clean, efficient, and maintainable code. /li /ul