Data Modeler

Key Responsibilities

• Data Architecture & Modeling: Design, develop, and maintain conceptual, logical, and physical data models tailored to the complexities of the retail industry (e.g., point-of-sale, inventory management, customer lifecycle, and e-commerce data).
• Dimensional Modeling (Kimball): Apply strict Kimball Concepts and methodologies to design robust star and snowflake schemas that support high-performing BI and reporting solutions.
• Medallion Architecture Implementation: Architect data flows utilizing the Medallion Architecture (Bronze, Silver, Gold layers) within our data lake/lakehouse environment to progressively refine and enrich raw data into business-ready assets.
• Semantic Layer Design: Design and govern a unified Semantic Layer to bridge the gap between complex data structures and business users. Ensure consistent business definitions, metrics, and dimensions across all BI platforms (e.g., Power BI, Tableau, Looker).
• Collaboration & Translation: Partner closely with data engineers, product managers, and retail business stakeholders (merchandising, supply chain, marketing) to translate complex business requirements into scalable data structures.
• Data Governance & Quality: Establish data modeling standards, maintain comprehensive data dictionaries, and ensure data integrity and compliance across all data products.

Qualifications

• Experience: 5+ years of dedicated experience in data modeling and data architecture, specifically within the Retail or FMCG industry.
• Methodology Mastery: Deep, demonstrable expertise in Dimensional Modeling and the Kimball lifecycle.
• Modern Data Architecture: Proven experience designing data models for cloud data warehouses/lakehouses (e.g., Snowflake, Databricks, BigQuery) utilizing the Medallion Architecture.
• Semantic Layer Expertise: Hands-on experience designing and implementing semantic layers or metrics layers (using tools like dbt, LookerML, AtScale, or SSAS).
• Technical Skills: Advanced SQL proficiency and experience with industry-standard data modeling tools (e.g., Erwin, Hackolade, Lucidchart, or SQLDBM).
• Retail Domain Knowledge: Strong understanding of retail-specific data domains, including basket analysis, inventory optimization, omni-channel sales, and customer 360.
• Communication: Excellent ability to communicate complex technical concepts to non-technical business stakeholders.