Data Modeler, POZNAŃ WROCŁAW
✔️Required Experience:
- 5+ years of experience in data modelling roles within Financial Services or Investment Banking
- Strong experience modelling data for analytics, reporting, risk, or regulatory use cases
- Proven track record working with data intensive and integration heavy environments
- Experience collaborating with business and technical stakeholders across delivery teams
✔️Key Skills & Competencies
- Strong knowledge of data modelling techniques (conceptual, logical, physical)
- Experience with dimensional modelling, star/snowflake schemas
- Exposure to Data Vault modelling (highly desirable for regulatory and audit use cases)
- Strong SQL skills to validate models and support data analysis
- Understanding of data lineage, metadata, and data quality principles
- Familiarity with modelling tools and documentation standards
✔️Technology & Platform Exposure
- Modern data platforms: Databricks / Lakehouse architectures
- Cloud data environments (e.g. Azure)
- Integration with ETL/ELT pipelines and analytical layers
- Collaboration tools: Jira, Confluence, version control systems
✔️Nice to Have
- Experience with Data Mesh concepts and domain oriented modelling
- Exposure to risk, credit, or regulatory reporting domains
- Background in working with Azure based analytics platforms
🔹 For our Client we are looking for Data Modeler🔹
Possible location in Poland : Poznań or Wrocław - 2 days/week onsite.
✔️About Insights and Data
We are looking for an experienced Data Modeler to support the design and evolution of enterprise grade data models for modern data platforms in Financial Services and Investment Banking. The role focuses on shaping logical and physical data structures that underpin analytics, risk, regulatory reporting, and business critical decision making.
The Data Modeler works closely with Business Analysts, Data Engineers, Architects, and governance teams to ensure data structures are consistent, scalable, auditable, and aligned with business and regulatory requirements.
,[Design and maintain conceptual, logical, and physical data models for enterprise data platforms, Translate business and regulatory requirements into robust data structures, Define and manage data entities, relationships, keys, and hierarchies across domains, Support data integration initiatives by defining source to target mappings and canonical models, Work closely with Data Engineers to ensure models are implemented correctly in lakehouse / warehouse environments, Ensure data models support regulatory reporting, lineage, auditability, and data quality requirements, Contribute to data standards, naming conventions, modelling best practices, and documentation] Requirements: Databricks, SQL, ETL, Azure, Jira