*PLEASE SEND UPDATED RESUMES TO [email protected] <[email protected]>*

Title : Data Modeler

Location: Rochester, NY/Webster, NY

Start date: 9th June 2014

Duration : 6-9 months



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.



Thanks and look forward to your prompt and positive response...



*Kiran Nallolla  *|  Sr. Account Manager

Office:   732 777 0800 ext. *518*



*BS**M*

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