Overview
Accountable for creating high quality Enterprise Data Warehouse data designs. This involves understanding and documenting the detail on where to source data, how the source data will be ingested into the data platform, data mappings and data modelling. The data designs include defining the details of any required data harmonization, calculations, derivations and transformation logic. The role involves occasional business trips to the office in Krakow, typically once a month or once a quarter, depending on team needs. Project Overview:
- Gather and analyze business requirements related to data warehousing.
- Act as the main point of contact between business stakeholders and the development team.
- Translate business requirements into clear data models, mappings, and functional documentation for Data Engineers.
- Design and document data ingestion flows from existing and new data sources.
- Collaborate with the Principal Data Designer on solution design and data modeling decisions.
- Ensure designs meet business needs, follow data design principles, and are delivered accurately and on time.
- Maintain and improve documentation standards, templates, and design processes.
- Review colleagues’ designs and provide constructive feedback.
- Support cross-team collaboration and help other workstreams when needed.
- Strong experience in data modeling for enterprise Data Warehouses.
- Good understanding of data warehousing concepts, data structures, and source-to-target mappings.
- Experience working as a bridge between business stakeholders and technical teams.
- Strong analytical skills with the ability to translate business needs into clear technical documentation.
- Experience preparing functional specifications, mappings, and design documents for development teams.
- Ability to work collaboratively in an Agile environment.
- Strong communication skills and confidence when working with stakeholders across different teams.
- High attention to detail and strong ownership of deliverables.
- Ability to challenge decisions constructively and contribute to design improvements.
- Proactive approach to learning and continuous improvement.
- Querying and transforming data using SQL, Python / PySpark.
- Conducting detailed data profiling and analysis.
- Modelling data into a complex facts and dimensions, identifying suitable mappings, aggregations and snapshot tables where appropriate.
- Identifying complex data quality rules, considering alternative sources and volumetric analysis.
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