Data Engineer, Analytics Architecture

Company Description
Ralph Lauren Corporation (NYSE:RL) is a global leader in the design, marketing and distribution of premium lifestyle products in five categories: apparel, accessories, home, fragrances, and hospitality. For more than 50 years, Ralph Lauren's reputation and distinctive image have been consistently developed across an expanding number of products, brands and international markets. The Company's brand names, which include Ralph Lauren, Ralph Lauren Collection, Ralph Lauren Purple Label, Polo Ralph Lauren, Double RL, Lauren Ralph Lauren, Polo Ralph Lauren Children, Chaps, among others, constitute one of the world's most widely recognized families of consumer brands. At Ralph Lauren, we unite and inspire the communities within our company as well as those in which we serve by amplifying voices and perspectives to create a culture of belonging, ensuring inclusion, and fairness for all. We foster a culture of inclusion through: Talent, Education & Communication, Employee Groups and Celebration.

Position Overview
Purpose & Scope: We are looking for a Data Engineer to join our Retail Analytics & Engineering team, focused on modernizing our data ecosystem by migrating legacy pipelines to Databricks and Prophecy, and driving business outcomes in planning, merchandising, and supply chain. In this role, you will work closely with Retail business users and Cloud Engineering teams to re-engineer legacy data workflows, build scalable pipelines, and deliver high-performance data products supporting retail operations. You will be responsible for both business-driven retail analytics use cases and technical platform migration and optimization, enabling our transition to a modern, cloud-native data architecture.

Essential Duties & Responsibilities
Retail Data Engineering:

• Build scalable pipelines supporting key retail analytics use cases such as sales forecasting, demand planning, inventory optimization, and logistics tracking.
• Integrate data from retail POS systems, eCommerce platforms, warehouse management systems, and 3PL providers.
• Develop retail data models for product hierarchies, planning attributes, and merchandising KPIs.
• Collaborate with Planning, Merchandising, and Supply Chain teams to translate business needs into data solutions.
• Enable real-time and batch data flows to drive operational dashboards and planning cycles. Cloud Migration & Platform Modernization:
• Lead the migration of existing on-premise or legacy ETL pipelines to Databricks and Prophecy.
• Re-architect pipelines for Spark-based execution, leveraging Databricks-native orchestration and Prophecy low-code development.
• Optimize Databricks Spark jobs for performance, cost, and scalability across retail datasets.
• Collaborate with platform teams to implement Unity Catalog, cluster policies, and CI/CD pipelines.
• Build monitoring, validation, and data quality frameworks for production-ready pipelines. General Engineering:
• Conduct performance tuning, troubleshooting, and root cause analysis of production data pipelines.
• Document migration patterns, retail data flows, and technical designs.
• Stay current with industry best practices in data engineering, retail analytics, and cloud data platforms.

Experience, Skills & Knowledge
Education & Experience:

• Proven experience in data engineering roles within a Retail, eCommerce, or Supply Chain domain.
• Hands-on experience with Databricks, Spark, and or similar data orchestration tools.
• Experience migrating ETL pipelines from legacy platforms (Informatica, SSIS, Talend, Glue) to modern cloud environments.
• Strong proficiency in Python, PySpark, and SQL.
• Experience working with Retail ERP systems (SAP, D365, JDA) and logistics data feeds.
• Familiarity with data modeling for retail hierarchies and planning processes.
• Exposure to cloud platforms (AWS or Azure preferred) and cloud-native data services.
• Experience with CI/CD for data pipelines and environment automation.
• Strong analytical skills and ability to troubleshoot complex data issues.
• Excellent communication and collaboration skills.