Why This Role Stands Out
This Senior Data Modeler role at Thinklusive offers a fantastic opportunity to shape enterprise data solutions within the mortgage industry, leveraging your extensive experience with relational and NoSQL databases. You'll thrive here if you possess a deep understanding of data modeling principles and a passion for designing robust, scalable data architectures. Join a collaborative team dedicated to data excellence and contribute to impactful projects.
Quick Overview
Job Description
Role: Senior Data Modeler (Mortgage)
Location: Mc Lean, VA (5Days onsite)
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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