Data Modeler/Data Warehousing expert

Responsibilities

• Data Modeling: Architect and document conceptual, logical, and physical data models using industry-standard methodologies (e.g., Star Schema, Snowflake Schema, Data Vault).
• Warehouse Strategy: Design, build, and optimize enterprise-grade data warehouse solutions that support complex analytical and reporting needs.
• Performance Tuning: Monitor and refine query performance, storage utilization, and processing efficiency within the data warehouse environment.
• ETL/ELT Architecture: Collaborate with Data Engineers to design efficient data movement pipelines from disparate sources into the data warehouse.
• Governance & Standards: Establish and enforce data modeling standards, naming conventions, and data lineage documentation to ensure high data quality and consistency across the organization.
• Stakeholder Collaboration: Partner with BI developers and business analysts to translate complex business requirements into high-performing data structures.

Skills

• Technical Proficiency: Extensive experience with modern data warehouse platforms (e.g., Snowflake, Google BigQuery, AWS Redshift, or Azure Synapse).
• Advanced SQL: Expert-level proficiency in SQL for complex data manipulation and performance optimization.
• Modeling Tools: Hands-on experience with tools such as ER/Studio, Erwin, or open-source modeling alternatives.
• Data Methodologies: Deep understanding of dimensional modeling, normalization/denormalization, and medallion architecture principles.
• Cloud Infrastructure: Familiarity with cloud-based storage strategies, partitioning, and indexing.