Data Modeling Analyst

Responsibilities

• Lead the development of conceptual, logical, and physical data models across multiple data domains.
• Define data modeling standards, naming conventions, and versioning practices to ensure consistency across the data ecosystem.
• Establish domain boundaries, core entities, master and reference data structures, and identify authoritative data sources.
• Develop model-to-contract mappings for data products, APIs, and operational data store (ODS) tables.
• Collaborate with Data Owners and Data Stewards to validate data definitions, rules, and governance standards.
• Contribute to data governance forums and decision‑making processes to ensure alignment with organizational data standards.
• Ensure data glossary completeness and lineage documentation through standardized cataloging practices.
• Design scalable physical data models optimized for modern cloud-based data platforms.
• Maintain documentation related to data architecture, metadata, lineage, and governance policies.
• Support initiatives that strengthen data quality, consistency, and enterprise data management practices.

• Strong hands‑on experience in conceptual, logical, and physical data modeling.
• Experience working with ER modeling and dimensional modeling techniques.
• Expertise in designing production‑grade physical data models on cloud platforms.
• Knowledge of data governance frameworks, data cataloging, and metadata management.
• Experience collaborating with Data Owners, Data Stewards, Data Quality teams, and governance councils.
• Familiarity with data mesh principles and domain‑oriented data architecture.
• Experience designing models optimized for modern analytical data platforms.
• Working knowledge of privacy‑by‑design principles, including data masking and pseudonymization rules.
• Experience using enterprise data modeling tools such as Sparx Enterprise Architect or similar platforms.

• Translate complex business requirements into structured and scalable data models.
• Align multiple domain viewpoints into a consistent canonical enterprise data model.
• Work effectively with cross‑functional teams across data, governance, and business units.
• Ensure strong data lineage, traceability, and metadata management practices.
• Balance technical data architecture considerations with business data needs.
• Communicate complex data concepts clearly to both technical and non‑technical stakeholders.

• The opportunity to contribute to enterprise‑scale data architecture and governance initiatives.
• A collaborative environment focused on data innovation and best practices.
• Exposure to modern cloud‑based data platforms and enterprise data ecosystems.
• Opportunities to shape data modeling standards and governance frameworks across the organization.
• A dynamic environment supporting continuous learning and professional growth within the data domain.

Want to discuss this opportunity in more detail? Feel free to reach out.