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A new Job, ID: 177716 was added at eJobsVille.com - For the Best Tech Jobs in Town
Title: Data Modeler - ErWin posted on 2014-05-29 12:27:53
Job Description:Purpose and Description of Role
A data modeler builds data models that describe data and its relationships in a consistent way to support business and technical objectives.
Responsibilities and Activities
· Analyzing business requirements as a guide to data modeling. Working with BSA and Business to clarify the requirements and validate data models and definitions.
· Understanding and following Enterprise Data Modeling standards, Approaches, and Tools (ErWin).
· Understanding Xerox Enter[rise Data Models (Conceptual and Logical) and modeling additional data within the structure of the Enterprise data model.
· Building data models with the flexibility to change when business requirements change.
· Understanding Enterprise Data Modeling standards, Approaches, and Tools (ErWin) and ensuring the proposed model follows data architecture guidelines and best practices.
· Rationalizing the relationships expressed in the logical data model to the schema of the physical data store (that is, database tables and columns).
Metrics for Evaluation
Data modelers typically report to a data architect. Their performance can be measured by:
· Compliance with data architecture guidelines and best practices.
Reuse of existing models.
· Extension of data architecture to new domains.
· User satisfaction.
· The speed or cost of extending data models to accommodate new uses.
Background
Data modelers typically have been database administrators or business analysts with IT skills. The experience and skills of a data modeler should include:
· Three years of experience with data modeling. Usually data modelers will have focused on modeling for transactional or analytic systems, which require distinct approaches.
· Ability to see the role of data models in supporting diverse use cases such as business processes, BI, data migration or master data management (MDM).
· Understanding of dimensional modeling, snowflakes, slowly changing dimensions, irregular dimensions, and surrogate, compound and intelligent keys.
· Understanding of multiple representational techniques in a data model so as not to lock data into one relationship.
· Experience in transactional and operational, analytical, and MDM modeling techniques.
Job Requirements: Purpose and Description of Role
A data modeler builds data models that describe data and its relationships in a consistent way to support business and technical objectives.
Responsibilities and Activities
· Analyzing business requirements as a guide to data modeling. Working with BSA and Business to clarify the requirements and validate data models and definitions.
· Understanding and following Enterprise Data Modeling standards, Approaches, and Tools (ErWin).
· Understanding Xerox Enter[rise Data Models (Conceptual and Logical) and modeling additional data within the structure of the Enterprise data model.
· Building data models with the flexibility to change when business requirements change.
· Understanding Enterprise Data Modeling standards, Approaches, and Tools (ErWin) and ensuring the proposed model follows data architecture guidelines and best practices.
· Rationalizing the relationships expressed in the logical data model to the schema of the physical data store (that is, database tables and columns).
Metrics for Evaluation
Data modelers typically report to a data architect. Their performance can be measured by:
· Compliance with data architecture guidelines and best practices.
Reuse of existing models.
· Extension of data architecture to new domains.
· User satisfaction.
· The speed or cost of extending data models to accommodate new uses.
Background
Data modelers typically have been database administrators or business analysts with IT skills. The experience and skills of a data modeler should include:
· Three years of experience with data modeling. Usually data modelers will have focused on modeling for transactional or analytic systems, which require distinct approaches.
· Ability to see the role of data models in supporting diverse use cases such as business processes, BI, data migration or master data management (MDM).
· Understanding of dimensional modeling, snowflakes, slowly changing dimensions, irregular dimensions, and surrogate, compound and intelligent keys.
· Understanding of multiple representational techniques in a data model so as not to lock data into one relationship.
· Experience in transactional and operational, analytical, and MDM modeling techniques.
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