Enterprise Data Modeler

Description:

Job Title: Enterprise Data Modeler
Location: Richmond, VA
Duration: Annual Contract (Potential extension beyond June 30, 2025)
Work Arrangement: Onsite 2-3 days/week (Local candidates only)
Interview Process: Onsite interviews (No exceptions)

Key Responsibilities:
Data Modeling:
Develop conceptual data models to identify key business entities and relationships.
Build logical and physical data models for client hierarchies and document data designs.
Perform data profiling and analysis to ensure accuracy and maintain data models.
Data Governance and Standards:
Establish and enforce governance processes to align implementation activities with defined architectures.
Define naming conventions, metadata standards, and enforce data quality processes.
Monitor data redundancy, completeness, and improvement.
Integration and Strategy:
Develop canonical models, data-as-a-service models, and SOA-based integrations.
Analyze data-related system integration challenges and recommend strategic solutions.
Create plans for data security, backup, disaster recovery, and regulatory compliance.
Collaboration and Communication:
Present modeling results and recommendations to internal stakeholders and development teams.
Collaborate with data architects and business teams to refine data models.
Facilitate workshops and provide clear documentation of processes and results.


Skills and Qualifications:
Required:
Experience:
10+ years of expertise in data modeling and data analysis.
Extensive experience with MDM tools and integrating source systems into MDM solutions.
Proven track record in developing data models for data warehouses, data lakes, and other data assets.
Technical Proficiency:
Advanced SQL skills and proficiency in query languages (Python, R, Scala).
Expertise in cloud-based data technologies such as Azure Synapse, Databricks, Snowflake, or SQL Server.
Strong understanding of SOA and enterprise data management practices.
Soft Skills:
Strong written and verbal communication skills.
Ability to present complex concepts in user-friendly language.
Excellent problem-solving and analytical skills with attention to detail.
Preferred:
Familiarity with data privacy practices and regulatory compliance.
Experience in data capacity planning, lifecycle management, and archiving strategies.