Job Title: Data Modeler - Direct Hire / Full Time / Perm
Job Location: Edison, NJ
Job Type: Full Time / Perm / Direct Hire + Benefits
“US citizens and those authorized to work in the US are encouraged to apply. We are unable to sponsor H1b candidates at this time.”
- Experience with Data Architect/Data Modeler.
- Finance industry data modeling experience.
- Experience with Netezza, Oracle PL/SQL.
- Experience with Data Profiling Tools.
- Experience with Data Modeling concepts.
- Experience with Metadata Tools.
- Experience with providing End User Query Support.
- Ability to support long term goals along with immediate performance concerns.
- Develop logical and physical data models
- Map logical entities to the enterprise Canonical Data Model
- Ensure data objects adhere to defined client standards and best practices
- Create logical data model using ER STUDIO or ERWIN as the modeling tool. Strong skills in Entity Relationship Modeling (ERWin modeling software preferred)
- Develop, test, and implement scripts, programs, and related processes to collect, manage, and publish DEV and PROD metadata from data models
- Develop DDL implementation/back-out plans
- Promote data models into production metadata environments
- Support DDL PROD implementation
- Support post-production validation of DDL
- Provide Level of Efforts (LOEs), activity status, identify dependencies impacting deliverables, identify risks and mitigation plans, identify issues and impacts
- Develops Logical and Physical data models to meet the needs of the organization's information systems.
- Design and implement of database schema.
- Maintains data integrity by working to eliminate redundancy.
- Maintaining a model repository.
- Delivery of complex enterprise data solutions with comprehensive understandings in Architecture, Security, Performance, Scalability, and Reliability
- Performing analysis on data requirements and data stored across multiple systems
- Documenting data lineage via source-to-target mapping documents, data workflows, etc
- Providing recommendations on data model enhancements.