Data Modeler

Data Modeler Hybrid

Richmond, VA Long term contract

Design enterprise data models (conceptual, logical, and physical) that enable trusted, reusable, and secure data across the organization.

Partners effectively with governance, engineering, architecture, analytics, security, and development teams.

Understands and proliferates modeling standards and patterns aligned to EDM, Data Governance, and industry practices.

Ensures data models support quality, lineage, privacy, and performance requirements.

Ensures models are implementable and optimize cost/performance in target platforms.

Ability to translate complex data concepts into clear business language; produce high-quality documentation and diagrams.

Keen Business Acumen: Quickly understands domain processes and how data supports decisions.

Critical/System Thinking: Able to see cross-domain dependencies and design for reuse and extensibility.

Has a keen attention to detail.

Intermediate/Advanced Microsoft Office Suite of Tools

Intermediate/Advanced Data Modeling Skills -- Conceptual, Logical, Physical; Relational (3NF), Dimensional (star/snowflake), Data Vault 2.0, wide tables for lake/ELT, and canonical models for integration.

Patterns & Techniques: Normalization/denormalization, SCD Type 1/2/3, surrogate keys, conformed dimensions, bridge/junk/helper tables, CDC handling, late/early arriving facts.

Metadata & Catalog: Business glossary, data dictionary, lineage; catalog tools integration.

Modeling Standards: Naming conventions, data types, constraints, keys, relationships, and versioning.

Reference & Master Data: Hierarchies, code sets, golden records; stewardship workflows.

Modeling Tools: Lucid Chart primary or ER/Studio/ERwin secondary.

Databases (OLTP/OLAP): SQL Server, Oracle, PostgreSQL, Snowflake, Azure Synapse, BigQuery, Redshift.

Data Lake & Files: Parquet, ORC, Delta Lake; partitioning, Z-ordering, clustering.

Cloud & Integration: Azure (Synapse, Fabric, SQL DB, Data Factory, Purview).

BI/Semantic Layer: Power BI, semantic models, measures, and calculation groups (alignment to the logical model).

Profiling & Quality: DQ; profiling and rule authoring.

Security/Privacy: Row-/column-level security, dynamic masking, tokenization/encryption, privacy impact assessment alignment.

SQL (intermediate): Window functions, CTEs, query optimization, index strategies, execution plans.

Minimum 3-5 years of Data Modeling experience with enterprise scale systems.

Minimum 1-2 years working in MS Azure environments and SQL databases (jSynapse, SQL DB, Data Lake).

Minimum 1-2 years of working with Power BI semantic modeling and KPI alignment.

Minimum 3-5 years of working with SQL.

Experience in banking, mortgage lending and rental housing preferred.