Data Architect - Modern Enterprise Data Platform - Retail experience is Mandatory

• Strong hands-on experience designing dimensional models, including facts, dimensions, conformed dimensions, grain definition, slowly changing dimensions, surrogate keys, business keys, and semantic-ready structures
• Practical experience with medallion architecture, lakehouse architecture, enterprise data warehouses, data lakes, and modern cloud data platforms
• Ability to analyze source systems, define source-to-target mappings, profile data, document business rules, and clarify ingestion, quality, and transformation requirements
• Experience with data modeling and metadata tools such as ERWIN, ER/Studio, or comparable platforms
• Clear communication skills with the ability to explain complex data architecture concepts in practical, business-relevant language, * Bachelors or masters degree in Computer Science, Information Technology, Data Engineering, Data Architecture, Engineering, or a related field.
• 5+ years of experience in data architecture, data modeling, data warehousing, data engineering, or enterprise data platform design.
• Strong hands-on experience designing dimensional models, including facts, dimensions, conformed dimensions, grain definition, slowly changing dimensions, surrogate keys, business keys, and semantic-ready structures.
• Practical experience with medallion architecture, lakehouse architecture, enterprise data warehouses, data lakes, and modern cloud data platforms.
• Ability to analyze source systems, define source-to-target mappings, profile data, document business rules, and clarify ingestion, quality, and transformation requirements.
• Strong SQL skills and the ability to review, interpret, and guide transformation logic across engineering and analytics teams.
• Experience with Snowflake, Databricks, Azure Data Factory, Azure Data Lake Storage, or comparable modern cloud data platform technologies.
• Experience with data modeling and metadata tools such as ERWIN, ER/Studio, or comparable platforms.
• Understanding of batch, streaming, CDC, API-based, file-based, incremental, merge, append-only, and historical load patterns.
• A collaborative style, strong judgment, and the ability to influence decisions across business, technical, governance, architecture, and engineering teams.
• Clear communication skills with the ability to explain complex data architecture concepts in practical, business-relevant language.

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