Enterprise Architect/TRENTON, NJ ( Hybrid )-12 months Contract

Building Data Architecture Frameworks, Data pipeline frameworks, Standards, and Data Modeling Activities for UI Mod All underlying systems, data owners, databases, andapplications working together with the Sr Data Management Lead and the Officeof the CIO in defining the Conceptual, Logical, Physical Analytical andcanonical models. KNOWLEDGE AND EXPERIENCE 15+yearsofexperience in Business/IT working as a Data Architect, Data Modeler, Data Analyst and/or Business Process Support Associate Expertise in Data Modeling, Solution Architecture, Master Data Management (MDM),Business Process Analysis/Engineering, Business Requirements Gathering and Systems Analysis in State, Local Government and Public Sector Domains Expertise in one or more Data Modeling Tools Experienced in working with different Software Development Methodologies. Extensive Knowledge of Data Management Principles Extensive Knowledge of Master Data Management Principles Knowledge of Data Governance Concepts Knowledge of Data Engineering Methodologies Knowledge of Data Engineering DUTIES and RESPONSIBILITIES Conduct Current State Assessment of Data Systems Define System of Record and Data Dictionary for Data Systems Develop Enterprise Conceptual, Logical and Physical Data Models Creates and builds Conceptual, Logical and Physical Models Facilitates Business Sessions and validates business taxonomy and use cases aligned to data models Provide initial design solution for core data model as well as for additional upcoming data sources and required models supporting Support Project Needs to design Conceptual Logical and Physical Models to Modernization Efforts Define the Client architecture from Conceptual, Logical to Physical layers. Reverse and forward engineer data models in Erwin or other Data Modeling Tools Optimize and update Conceptual, logical and physical data models to support new and existing projects and ensure business users are working in a clean safe data environment. Assist in the creation of best practices for standard naming conventions and coding practices to ensure consistency of data models.

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