Data Modeler - Richmond, United States - Crescens Inc.

Description

• The Client Information Technology Division is seeking a senior Data modeler to develop Data models for Data Assets and implementation of a cloud-based data management platform that will support the agency.
• Enterprise data modeler provides expert support across the enterprise information framework, analyze and translate business needs into long-term solution data models by evaluating existing systems and working with a business and data architect to create conceptual data models , data flows .
• Develop best practices for Data Asset development, ensure consistency within the system and review modifications of existing cross-compatibility systems. Optimize data systems and evaluate implemented systems for variance discrepancies and efficiency.
• Maintain logical and physical data models along with accurate metadata.

• Create conceptual data model to identify key business entities and visualize their relationships, define concepts and rules.
• Translate business needs into data models Build logical and physical data models for client hierarchy Document data designs for team.
• Present and communicate modeling results and recommendations to internal stakeholders and Development teams and explains features that may affect the physical data model.
• Ensure and enforce a governance process to oversee implementation activities and ensure alignment to the defined architecture.
• Perform data profiling/analysis activities that helps to establish, modify and maintain data model.
• Develop canonical models, Data as a service models and Knowledge of SOA to support integrations.
• Analyze datarelated system integration challenges and propose appropriate solutions with strategic approach.
• 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; take steps to assure metadata accuracy and validity.
• Establish methods and procedures for tracking data quality, completeness, data redundancy, and improvement.
• Conduct data capacity planning, life cycle, duration, usage requirements, feasibility studies, and other tasks.
• Create strategies and plans for data security, backup, disaster recovery, business continuity, and archiving.
• Ensure that data strategies and architectures are in regulatory compliance.
• Good knowledge of applicable data privacy practices and laws.
• Strong written and oral communication skills. Strong presentation and interpersonal skills and Ability to present ideas in user-friendly language.
• Experience in writing queries (SQL, Python, R, Scala) as needed and experience with various data technologies such as Azure Synapse or SQL Server, Snowflake, Databricks

• Create conceptual data model to identify key business entities and visualize their relationships, define concepts and rules.
• Translate business needs into data models Build logical and physical data models for client hierarchy Document data designs for team.
• Present and communicate modeling results and recommendations to internal stakeholders and Development teams and explains features that may affect.
  Develop canonical models, Data as a service models and Knowledge of SOA to support integrations.
• Perform data profiling/analysis activities that helps to establish, modify and maintain data model
• Analyze data-related system integration challenges and propose appropriate solutions with strategic approach.
• Perform data profiling and analysis for maintaining data models Develop and support the usage of MDM toolkit Integrate source systems into the MDM sol
• 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; take steps to assure metadata accuracy and validity
• Establish methods and procedures for tracking data quality, completeness, data redundancy, and improvement.
• Conduct data capacity planning, life cycle, duration, usage requirements, feasibility studies, and other tasks.
• Experience in ERWIN tool
• Experience in NIEM model