GCP Data Modeler
New
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XebiaData platforms
Work from the European Union regionContractSenior
Salary120 - 180 PLN per hour net b2b
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Job Details
- Languages
- Fluent English
- Experience
- Extensive senior-level experience in Data Modeling
- Required Skills
- SQLData engineeringData modeling
Requirements
- Bring extensive senior-level experience in Data Modeling.
- Have proven experience designing analytical or enterprise data models.
- Have hands-on experience with Google Cloud Platform.
- Have practical experience with dbt Core, including defining model structures, dependencies, and development standards.
- Have strong knowledge of SQL-based data environments.
- Have experience with cloud-based data platforms and modern data architectures.
- Understand Data Quality concepts, controls, and monitoring solutions.
- Be able to analyze existing data architecture and design a clear, scalable target-state model.
- Have experience supporting data migration or transformation initiatives and collaborating closely with Data Engineering teams.
- Be able to define and document modeling standards, naming conventions, and integration principles.
- Have strong analytical and problem-solving skills and the ability to create clear technical and maintenance documentation.
- Communicate fluently in English and collaborate with technical and non-technical stakeholders.
- Be able to work independently and take ownership of data modeling decisions.
- Have practical experience using AI-powered assistants such as ChatGPT, Claude, or Copilot in analytical and documentation work.
Responsibilities
- Analyze the existing Data Quality monitoring solution, including its data structures, relationships, dependencies, and integration points.
- Design target-state analytical and enterprise data models supporting Data Quality monitoring in GCP.
- Define data structures, modeling patterns, and development standards within dbt Core.
- Contribute to the design of the target cloud-native monitoring architecture.
- Establish data modeling standards, naming conventions, reusable patterns, and integration principles.
- Map legacy data structures and processes to the target architecture, identifying modeling gaps, dependencies, and migration risks.
- Collaborate with Business Analysts, Data Engineers, and Test Engineers on requirements, implementation, validation, reconciliation, and testing.
- Review implemented data structures for alignment with agreed models and standards.
- Create and maintain data model documentation, mapping specifications, data dictionaries, and modeling guidelines.
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