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Ref#
W163157
Bundesstaat/Region
Karnataka
Abteilung
IT (EDV)
Ort
India
Stadt
Bangalore

Beschreibung des Unternehmens

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.

Positionsübersicht

Purpose & Scope: 
As part of Ralph Lauren’s Data Strategy initiative, we are seeking a Senior Data Engineer to lead the design and delivery of scalable data products that power analytics across commercial, regional, and supply chain domains.
This role focuses on building and optimizing large-scale data pipelines, designing high-performance data models, and ensuring reliable data access across BI, analytics, and data science platforms. The Senior Data Engineer will play a critical role in shaping engineering standards, guiding platform modernization, and enabling trusted, high-quality analytics across the organization.
You will partner closely with Data Product Managers, Data Architects, Analytics teams, and Cloud Platform Engineering to translate complex business requirements into scalable technical solutions.

Wesentliche Aufgaben und Verantwortlichkeiten

Data Pipeline Architecture and Development
  • Design and build scalable, high-performance ETL and ELT pipelines that transform raw enterprise data into analytics-ready datasets.
  • Lead the development of reliable batch and near real-time data processing frameworks supporting operational reporting and advanced analytics.
  • Establish engineering patterns for reusable, modular data pipelines across multiple business domains.
  • Ensure pipelines are production-grade with appropriate testing, observability, and operational monitoring
Data Product Engineering
  • Design and deliver domain-level data products supporting retail, planning, merchandising, and supply chain analytics.
  • Structure datasets to support diverse consumption patterns including BI reporting, advanced analytics, and data science workloads.
  • Enable consistent, governed data access across enterprise analytics platforms.
Data Modeling and Analytics Enablement
  • Design scalable data models such as star and snowflake schemas optimized for analytical workloads.
  • Build analytics-ready datasets supporting key business metrics including sales, inventory performance, demand planning, and operational KPIs.
  • Ensure data models are optimized for efficient integration with BI tools such as Power BI.
Performance, Scalability, and Platform Optimization
  • Optimize data processing frameworks for performance, scalability, and cost efficiency across large datasets.
  • Troubleshoot complex pipeline and infrastructure issues, identifying root causes and implementing sustainable solutions.
  • Collaborate with platform engineering teams to improve cluster configuration, storage design, and processing efficiency.
Data Governance and Quality
  • Implement robust data quality frameworks including validation, reconciliation, and monitoring of production datasets.
  • Support enterprise governance initiatives including metadata management, lineage, and data catalog integration.
  • Ensure data engineering solutions adhere to organizational security and compliance standards.
Collaboration and Technical Leadership
  • Work closely with Data Product Managers, Data Analysts, and Data Scientists to translate business needs into scalable engineering solutions.
  • Provide technical guidance and mentorship to junior data engineers.
  • Contribute to engineering standards, architectural patterns, and platform best practices across the data organization.
Modern Data Platform Enablement
  • Develop and deploy data solutions on modern cloud data platforms including Databricks and Microsoft Azure services such as Azure Data Factory and Synapse Analytics.
  • Lead modernization initiatives transitioning legacy data pipelines to scalable cloud-native architectures.
  • Contribute to CI/CD implementation and automation of data engineering workflows.






ERFAHRUNG, FÄHIGKEITEN UND KENNTNISSE

  • Bachelor’s degree in Computer Science, Engineering, or a related field. Master’s degree is preferred.
  • Significant experience in data engineering roles designing and delivering large-scale data pipelines and analytics datasets.
  • Strong proficiency in SQL and Python with hands-on experience using Spark-based distributed data processing frameworks.
  • Experience working with modern cloud data platforms, preferably Microsoft Azure and Databricks.
  • Proven experience designing data models that support enterprise analytics and BI workloads.
  • Experience implementing CI/CD practices and automated deployment pipelines for data platforms.
  • Strong understanding of data governance concepts including data lineage, metadata management, and data quality monitoring.
  • Excellent analytical and troubleshooting skills with the ability to resolve complex data engineering challenges.
  • Strong collaboration and communication skills working across engineering and business teams.

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