Data Modeler

Data Modeler Location – Midtown Manhattan, NYC- preferred at least 4 days onsite Position Summary We are seeking an experienced Data Modeler to design, develop, and maintain scalable enterprise data models that support analytics, reporting, and operational data needs. The ideal candidate possesses strong expertise in dimensional modeling, modern cloud data platforms (particularly Snowflake), and data warehousing best practices. This role requires close collaboration with business stakeholders, data engineers, analytics teams, and solution architects to translate business requirements into robust, well-documented data structures. The successful candidate will combine strong modeling and documentation skills with practical coding experience to support data transformation, validation, and optimization efforts. Key Responsibilities Data Modeling & Architecture • Design and maintain conceptual, logical, and physical data models for enterprise data platforms. • Develop dimensional models using Kimball methodologies, including star schemas, fact tables, dimension tables, slowly changing dimensions (SCDs), and conformed dimensions. • Partner with business and technical stakeholders to understand data requirements and translate them into scalable data structures. • Establish and maintain data modeling standards, naming conventions, and governance practices. • Evaluate source systems and define integration strategies for enterprise reporting and analytics. Snowflake & Data Warehousing • Design and optimize Snowflake database structures, including schemas, tables, views, and data organization strategies. • Collaborate with Data Engineering teams to implement efficient ELT/ETL processes. • Support performance tuning, query optimization, clustering strategies, and cost-efficient Snowflake design. • Participate in data platform modernization initiatives and cloud migration efforts. Documentation & Governance • Create and maintain comprehensive data dictionaries, business glossaries, lineage documentation, and model diagrams. • Document data definitions, transformation rules, business logic, and source-to-target mappings. • Support data governance initiatives by promoting consistency, quality, and traceability across data assets. • Ensure data models align with regulatory, security, and compliance requirements. Development & Technical Support • Write and review SQL, data transformation logic, and validation queries. • Support development of data pipelines and transformation processes using SQL and modern data engineering tools. • Collaborate with developers and analysts to troubleshoot data quality and performance issues. • Build reusable frameworks and standards that improve data consistency and maintainability. Required Qualifications • Bachelor's degree in Computer Science, Information Systems, Data Analytics, or related field. • 5+ years of experience in data modeling, data warehousing, or data architecture roles. • Strong expertise in dimensional modeling and Kimball data warehousing principles. • Hands-on experience with Snowflake in a production environment. • Advanced SQL development and query optimization skills. • Experience creating conceptual, logical, and physical data models. • Strong documentation skills, including data dictionaries, lineage, and technical specifications. • Experience working with ETL/ELT processes and modern data pipelines. • Ability to communicate complex data concepts to both technical and non-technical audiences. Preferred Qualifications • Experience with dbt, Matillion, Informatica, Fivetran, Airflow, or similar data integration tools. • Experience with data cataloging and governance platforms such as Collibra, Alation, or Microsoft Purview. • Familiarity with Data Vault modeling concepts. • Experience with Python, PySpark, or other programming languages used in data engineering workflows. • Experience working in Agile delivery environments. • Knowledge of healthcare, financial services, insurance, or other highly regulated industries. Technical Skills Required • Snowflake • SQL • Dimensional Modeling • Kimball Methodology • Data Warehousing • Data Mapping & Lineage • Data Documentation • Data Quality Analysis Preferred • Python • dbt • Spark/PySpark • Azure, AWS, or GCP • Git/GitHub • Data Governance Tools • BI Platforms (Power BI, Tableau, Looker) Success Factors • Strong analytical and problem-solving skills. • Ability to balance business requirements with scalable technical design. • Attention to detail and commitment to data quality. • Excellent documentation and communication skills. • Comfortable working across architecture, engineering, analytics, and business teams. • Ability to influence enterprise data standards and best practices. Nice-to-Have Profile A candidate who can move comfortably between architecture and implementation—someone who can design a Kimball-compliant dimensional model in the morning, document source-to-target mappings in the afternoon, and write SQL/Python code to validate and support the implementation before the day is over.