Why This Role Stands Out
This role offers a fantastic opportunity to leverage your extensive data modeling expertise within a reputable company, driving critical data architecture decisions and contributing to impactful projects. If you excel in designing conceptual, logical, and physical data models across relational and NoSQL databases, and thrive in a collaborative, on-site environment, this is a chance to further develop your skills and advance your career. Apply today to explore this exciting position!
Quick Overview
Job Description
Role : Senior Data Modeler
Client: Hexaware/Freddie Mac
Location: McLean, VA (5days onsite)
Experience: 10+ Above
Required Skills:
Experience: 10+ years in Data Modeling, Data Architecture, or Data Management.
Strong experience in Data Modeling using relational and NoSQL databases.
Expertise in Conceptual, Logical, and Physical Data Model design.
Hands-on experience with Dimensional Modeling (Star/Snowflake Schemas) and Data Warehousing.
Experience in Data Mapping, Data Lineage, Data Profiling, and Data Quality Analysis.
Proficiency in schema design, DDL creation, XML/JSON data structures, and metadata management.
Experience with data modeling tools such as ER Studio or equivalent.
Knowledge of Snowflake and cloud-based data platforms.
Familiarity with Agile development methodologies.
Strong analytical, problem-solving, and stakeholder communication skills.
Key Responsibilities:
Design, develop, and maintain enterprise data models to support business and analytical needs.
Gather and document data requirements from business and technical stakeholders.
Create and enhance logical and physical data models, schemas, and database objects.
Perform data profiling, lineage analysis, and source-to-target mapping.
Analyze and resolve data quality issues through root cause analysis.
Develop data flow diagrams, process workflows, and technical documentation.
Support data governance, metadata management, and data standardization initiatives.
Collaborate with cross-functional teams to deliver scalable, high-quality data solutions.
Ensure data models are optimized for performance, scalability, and maintainability.
Participate in peer reviews and contribute to data architecture best practices.
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