Data Engineer

About Ralph Lauren

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 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.

Job Summary

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.

Data Pipeline Design and Development

• Design, build, and maintain scalable ETL and ELT pipelines that transform raw enterprise and retail data into analytics-ready datasets.
• Support both batch and near real-time data processing to enable operational reporting, planning cycles, and analytical insights.
• Ensure pipelines are reliable, testable, and production-ready with appropriate validation and monitoring.

Data Modeling and Analytics Enablement

• Design and maintain data models such as star and snowflake schemas that support efficient querying and analytics consumption.
• Develop analytics-ready datasets aligned to retail KPIs including sales, inventory, demand, and supply chain performance.
• Optimize data structures to ensure seamless integration with BI tools such as Power BI and downstream analytics platforms.

Performance and Scalability Optimization

• Optimize data pipelines and storage layers for performance, scalability, and cost efficiency.
• Identify and resolve performance bottlenecks across data processing and consumption layers.
• Support platform-level optimization in collaboration with cloud and data platform teams.

Collaboration and Data Product Delivery

• Partner with Data Product Managers, Analysts, and Data Scientists to understand business requirements and deliver data products that serve diverse user needs.
• Work closely with Planning, Merchandising, and Supply Chain teams to translate functional requirements into technical solutions.
• Contribute to shared data standards, reusable patterns, and best practices across the analytics organization.

Data Governance and Quality

• Implement data quality, validation, and reconciliation checks to ensure trust and reliability of analytics data.
• Adhere to enterprise standards for data governance, security, and compliance.
• Support metadata, lineage, and documentation practices to improve transparency and maintainability.

Modern Data Platform Enablement

• Build and operate data solutions using modern cloud data platforms and services, including Databricks, Azure Data Factory, Synapse Analytics, and Azure SQL.
• Support migration and modernization initiatives as legacy pipelines are transitioned to cloud-native architectures.
• Contribute to CI/CD practices and environment automation for data pipelines.

Qualifications

• Proven experience in data engineering roles within a Retail, eCommerce, or Supply Chain domain.
• Hands-on experience with Databricks, Spark, and Prophecy.io 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.