Sr Data Modeler

Sr Data Modeler (12+ Years) Princeton, NJ / Columbia, SC 12+ Months Longterm Contracting basis

Role Description:
We are seeking a highly skilled Data Modeller to support enterprise data initiatives across both Data Warehouse (DWH) and Operational Data Store (ODS) environments. The role involves interpreting business requirements, designing end-to-end data models, and enabling scalable, efficient, and integrated data solutions. The ideal candidate will have deep expertise in dimensional and normalized modeling techniques, hands-on experience with data modeling tools, and a strong understanding of data warehousing concepts, ODS integration patterns, and modern data platforms.
This role requires close collaboration with business analysts, data architects, engineers, and stakeholders to ensure that data models support both operational and analytical needs while adhering to enterprise standards and governance policies.

Key Responsibilities:
Proven experience in designing and developing conceptual, logical, and physical data models for both Data Warehouse (DWH) and Operational Data Store (ODS) environments.
Ability to analyze and interpret Business Requirement Documents (BRDs) and translate business needs into scalable and optimized data models.
Deep understanding of data warehousing concepts, including ETL processes, star/snowflake schemas, SCD (Slowly Changing Dimensions), aggregations, and historical data tracking.
Strong grasp of Kimball (dimensional modeling) and Inmon (normalized modeling) methodologies; Data Vault modeling experience is a plus.
Experience designing ODS models that consolidate and normalize data from multiple upstream operational systems, maintaining current-state records and supporting near real-time data needs.
Ability to manage source system variability, handle schema harmonization, and design models that support ODS staging, cleansing, and integration processes.
Proficient with data modeling tools such as Erwin, ER/Studio, or IBM InfoSphere Data Architect to design models across databases like Snowflake, SQL Server, Oracle, and AWS Redshift.
Strong SQL and database design skills with an emphasis on performance optimization and efficient querying.
Experience collaborating with data architects, data engineers, BI teams, and business stakeholders to ensure model alignment with both operational and analytical objectives.
Strong problem-solving skills to analyze and resolve data issues, ensure data consistency, and support data governance and metadata management practices.
Ability to define and enforce data modeling standards, best practices, and version control across enterprise-wide models.
Comfortable working in Agile or hybrid delivery environments, with experience in iterative model evolution and data pipeline integration.

Required Skills and Experience:
12+ years of hands-on experience in relational, dimensional, and/or analytic data modeling, utilizing RDBMS, NoSQL platforms, and ETL/data ingestion protocols.
Proven experience working with data warehouses, data lakes, and enterprise big data platforms, particularly in multi-data-center environments.
Strong knowledge of metadata management, data modeling, and proficiency with modeling tools such as Erwin, ER Studio, or similar tools.
Expertise in performance tuning for queries and database applications to optimize efficiency and ensure scalability.
In-depth understanding of database design principles, data modeling techniques, and relational database concepts.
Demonstrated ability to troubleshoot and debug SQL and PL/SQL scripts, ensuring the stability and reliability of database solutions.

Preferred Qualifications:
Bachelor s or master s degree in computer/data science technical or related experience.
Experience in cloud data platforms (e.g., Snowflake, AWS, Azure) for data modeling and database management.
Familiarity with Big Data technologies and modeling for unstructured data.
Experience working with data governance frameworks and ensuring compliance with data quality and security standards.
Knowledge of Agile development methodologies.