Job Description & How to Apply Below
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/lake houses (e.g., Snowflake, Databricks, Big Query) 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.
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