Data Model Architect: Enterprise Semantics & Lakehouse

Virtusa; Minneapolis (MN); On-site; USD 110,000 - 140,000; Full time

Job summary

Virtusa in Minneapolis seeks a Data Modeler to translate logical models into Bronze, Silver, and Gold data products, including lakehouse tables and ontology representations. You will define entities, attributes, and keys, collaborating with data engineers to implement robust, governed data structures.

You will ensure proper architectural modeling (conceptual to physical), maintain lineage, and support semantic layers for analytics across enterprise data products and knowledge graphs.

Qualifications

• Strong SQL knowledge.
• Experience with conceptual, logical, and physical data modeling.
• Ability to map ontology concepts to identifiable data entities.
• Knowledge of lakehouse/warehouse modeling is a plus.

Responsibilities

• Define entities, attributes, relationships, cardinality, and business keys.
• Translate logical models into Bronze event tables with data engineers.
• Develop silver conformed entities and gold analytical data products.
• Create and manage star schemas, cube.dev models, and aggregate tables.
• Maintain semantic layers and data lineage across models.
• Ensure data grains avoid double-counting and support governance.

Skills

Conceptual data modeling

Logical data modeling

Physical data modeling

Relational modeling

Dimensional modeling

Event and canonical-data modeling

Temporal and bitemporal modeling

Master and reference data

Data normalization/denormalization

Metadata and lineage

Model governance/versioning

Strong SQL

Cube.dev models

Tools

ER/Studio

erwin

Sparx

Archi

Job description

Virtusa in Minneapolis seeks a Data Modeler to translate logical models into Bronze, Silver, and Gold data products, including lakehouse tables and ontology representations. You will define entities, attributes, and keys, collaborating with data engineers to implement robust, governed data structures.

You will ensure proper architectural modeling (conceptual to physical), maintain lineage, and support semantic layers for analytics across enterprise data products and knowledge graphs.