Drive the transformation of an established data warehouse into a modern, cloud-native architecture on AWS. This role centers on redefining data structures using dimensional and analytical modelling techniques, ensuring models meet both technical standards and business needs. You'll evaluate legacy ETL workflows, redesign data pipelines, and validate outputs to guarantee accuracy during migration.
Key Responsibilities
- Examine current data warehouse schemas, ETL logic, and reporting datasets to map data flows and extract business rules.
- Develop and document dimensional and analytical models that align with operational and reporting requirements.
- Create detailed source-to-target mapping specifications to guide data transformation.
- Validate new data models and pipelines through reconciliation and testing, ensuring consistency with legacy systems.
- Work closely with data engineers, BI developers, and business teams to ensure models support downstream analytics.
- Produce clear documentation, including data lineage, definitions, and model diagrams.
- Help shape and refine modelling standards, data governance, and quality practices across the initiative.
What You Bring
- Deep knowledge of data modelling frameworks such as Kimball and dimensional design.
- Proven track record in data warehouse development or migration projects.
- Hands-on experience building analytical and reporting models.
- Background in ETL/ELT development, including reverse-engineering existing logic.
- Advanced SQL proficiency for data transformation, validation, and reconciliation.
- Ability to turn business requirements into scalable, well-structured data models.
- Strong documentation skills, including data dictionaries and model diagrams.
- Experience working in Agile environments (Scrum or Kanban).
Nice to Have
- Familiarity with dbt for data transformation workflows.
- Industry experience in telecoms or regulated sectors like financial services or healthcare.
- Exposure to AWS data services such as Redshift, Glue, or S3.
- Understanding of BI tools and how data models enable reporting.
- Knowledge of data governance, metadata management, and lineage tracking.
