The Enterprise Data Modeler will support the Virginia Department of Transportation (VDOT) by developing data models for data assets and implementing a cloud-based data management platform. This role involves analyzing business needs, creating conceptual and physical data models, ensuring data consistency, and optimizing data systems. The data modeler will also be responsible for maintaining data models and metadata, as well as establishing data governance processes.
Location: Washington, District of Columbia, Virginia, United States,
Responsibilities:
- Create conceptual data models to identify key business entities and visualize their relationships.
- Translate business needs into data models and build logical and physical data models.
- Document data designs for the team.
- Present modeling results and recommendations to stakeholders and development teams.
- Ensure and enforce a governance process for implementation activities.
- Perform data profiling/analysis to maintain data models.
- Develop canonical models, Data as a service models, and utilize knowledge of SOA to support integrations.
- Analyze data-related system integration challenges and propose solutions.
- Perform data profiling and analysis for maintaining data models.
- Develop and support the usage of MDM toolkit.
- Integrate source systems into the MDM solution.
- Implement business rules for data reconciliation and deduplication.
- Enforce data models and naming standards across deliverables.
- Establish processes for governing the identification, collection, and use of corporate metadata.
- Establish methods and procedures for tracking data quality, completeness, data redundancy, and improvement.
- Conduct data capacity planning, life cycle, duration, usage requirements, and feasibility studies.
- Create strategies and plans for data security, backup, disaster recovery, business continuity, and archiving.
- Ensure that data strategies and architectures are in regulatory compliance.
Required Skills & Certifications:
Preferred Skills & Certifications:
Special Considerations:
Scheduling:
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