Hi,
Please forward resume for the below req
Role:: Enterprise Data Modeler (Alternative Investments Domain)
Location:: New York, NY (5 days in client office)
Duration: 6 months
Enterprise Data Modeler Overview
We are seeking an experienced Enterprise Data Modeler/Engineer to provide expert support across our enterprise information framework. The successful candidate will analyze and translate business needs into long-term solution data models, evaluate existing systems, and work with Alternative technology teams and business stakeholders to create conceptual and technical data models, schemas, and data flows.
Responsibilities
- Create conceptual data models to identify key business entities and visualize their relationships, defining concepts and rules.
- Design, develop, test, and publish canonical data models in detail.
- Proficiency in data modeling tools such as ER/Studio, ERwin, or similar.
- Familiarity with data warehousing concepts, data normalization techniques, and data security, encryption, and compliance requirements in the cloud.
- Proficiency in SQL, including writing complex queries, stored procedures, and functions.
- Experience with NoSQL databases and understanding of their data modeling techniques.
- Present and communicate modeling results and recommendations to internal stakeholders and Development teams, explaining features that may affect the physical data models.
- Collaborate with engineers and other stakeholders to gather requirements and translate them into data models.
- Knowledge of ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes.
- Create comprehensive documentation for data models, data dictionaries, and design specifications.
Required Qualifications
- Business and Financial domain knowledge of Alternative investment lifecycle and their data is essential.
- 8+ years of demonstrated, hands-on data modeling and enterprise software design experience in multiple industries for OLTP (relational) and analytical systems using relational databases.
- Strong knowledge of data modeling principles and standard methodologies, including a good understanding of canonical and semantic data modeling concepts.
- Ability to quickly grasp technological and business concepts.