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Metro locations, broad common data skills, and a recognizable consulting brand increase applicant competition.
Core data modelling skills transfer across industries, but banking/regulatory preference raises domain specificity.
No explicit years but multiple mandatory tools and domain skills make screening moderately strict.
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Design, develop, and maintain enterprise data models including conceptual, logical, and physical models for data warehouses, lakes, operational systems, and analytics platforms.
Collaborate with business stakeholders, data architects, and engineers to translate complex business and data requirements into scalable, governed data structures.
Support data governance, data dictionary creation, metadata management, source-to-target mapping, and ensure compliance with architecture and security standards.
Strong hands-on experience in data modelling including conceptual, logical, and physical models.
Proficiency with data modelling tools (ERwin, ER/Studio, PowerDesigner, etc.) and strong SQL skills.
Experience with modern data platforms such as Snowflake, Databricks, Oracle, SQL Server, or PostgreSQL and cloud platforms (Azure, AWS, GCP preferred).
Bachelor’s degree in Computer Science, IT, Data Engineering or related field or equivalent experience; work experience required: Not explicitly mentioned in the JD.
Experienced in financial services or regulated industries, preferably private banking, with knowledge of industry-standard data models and Data Vault methodology.
Comfortable with data governance tools (Collibra, Alation, Informatica, Microsoft Purview) and modern cloud-native data architectures.
Skilled in bridging business terminology and technical data modelling with strong analytical, documentation, and stakeholder management capabilities.