Ralph Lauren Data Engineer

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.