回复: IT职位信息
reply-to
chris@nexstaf.com
date Aug 2, 2007 11:04 AM
subject Sr. Data Modeler - downtown/LDM/IM/DW/finance exp
Please review the position description below and forward an updated resume if you feel you are both qualified and available. If not, please forward to any suitable candidate:
Location: Downtown
Position Type: PERM
Position: Sr. Data Modeler
Salary: 80 - 90K
Start Data: Immediate
Our client, a large Canadian Financial Institution located in downtown Toronto is actively seeking an experienced Senior Data Modeler for a Permanent Role.
Key Accountabilities / Activities:
PURPOSE OF JOB
Lead and consult on the design, implementation and integration of data warehouses. Develop datawarehouse data models to meet business requirements. Define data requirements and business rules. Perform physical data modeling.
MAJOR ACTIVITIES:
·Lead the overall design of the data warehouse
·Construct and maintains the data processing back-end; ensures data is current and meets quality and integrity standards and provides consistency and synchronization across all platforms.
·Plan for collection method, data cleansing, normalization and de-normalization of enterprise data to support decision making and reporting.
·Assure integrity of data; develops policies and procedures related to development and support of new and ongoing systems.
·Coordinate data models, dictionaries, and other database documentation across multiple applications.
Requirements:
KNOWLEDGE/SKILL REQUIREMENTS:
Subject Matter Expertise:
- Financial Industy experience a must (Brokerage)
·Logical Data Modeling--Knowledge of activities, tasks, processes, deliverables and techniques for analyzing and documenting conceptual/logical data models to capture domain knowledge and business rules.
·Information Management--Knowledge of organization's existing and planned Information Architecture and Information Management (IM) Methodology.
·Data Warehouse-Extensive knowledge of principle, design, construction and operation of large data warehouse
Extensive Work Experience:
·Modeling: Data, Process, Events, Objects--Knowledge of activities, tasks, processes, deliverables and techniques for analyzing and documenting logical relationships among the data, process or events.
·Accuracy/Attention to Detail--Ability to process information with high levels of accuracy.
·Relationship Management--Ability to establish and build healthy working relations and partnerships with clients, vendors and peers.
·Leadership--Knowledge of approaches, tools, and techniques for gaining the cooperation and support of others.
·Influencing--Ability to impact decisions within and outside own organization.
Work Experience:
·Problem Solving--Knowledge of approaches, tools, techniques for recognizing, anticipating, and resolving organizational, operational or process problems.
·Interpersonal Relationships--Knowledge of approaches, tools and techniques for working with individuals and groups in a constructive and collaborative manner.
·Information Management--Knowledge of organization's existing and planned Information Architecture and Information Management (IM) Methodology.
·Decision Making and Critical Thinking--Knowledge of tools and techniques for effective use of a broad range of factors, assumptions, frameworks and perspectives when solving problems.
·Database Structures--Knowledge of a database management structures (relational, hierarchical., distributed...) and associated platforms.
·Data Movement Tools--Knowledge of tools, techniques and practices for movement of electronic data.
JOB COMPLEXITIES/CHALLENGES:
·Analyzing business requirements to determine appropriate data warehouse architecture and design
·Working according to data warehouse industry best practices and bank standards
·Assessing need for data mart versus data warehouse; assessing impact of change of source system.
·Extracting, analyzing and abstracting data requirements from received information to generate a logical data model that represents the business data needs and business rules; evaluating different alternatives to generate physical data models.
·Managing scope creep in a project (changes in expected data volumes or how tables are to be used) which can have huge impact on data models; physical design and existing developed code resulting in increased complexity and costs to the project..
·Errors in data models can impact project deadlines and cost of repair could be high if discovered late in testing or production.