Ralph Lauren
Website:
ralphlauren.com
Job details:
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.
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The Senior Data Engineer designs, builds, and operates scalable data pipelines and curated datasets that power Ralph Lauren’s enterprise Data Products, analytics, and AI use cases, while providing technical depth and guidance to less experienced engineers on the team.
This role works closely with Data Engineering leadership, Data Product Managers, and platform teams to deliver reliable, well-governed, and reusable data assets, and is often looked to for design decisions on complex pipelines and data models.
The role is hands-on and execution-focused, with accountability for code quality, data reliability, and operational readiness, alongside a growing role in shaping engineering standards and mentoring junior engineers.
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- Data Pipeline Development & Design
Design and build complex data pipelines for ingestion, transformation, and delivery on modern data platforms.
Lead the design of reusable transformation logic and scalable data models that follow established engineering standards.
Make technical design decisions for curated datasets, balancing performance, reliability, and reusability.
Review code and designs from other engineers to maintain quality and consistency.
- Data Quality & Governance Support
Implement and improve automated data validation and quality checks as part of pipeline development.
Lead investigation and resolution of complex data quality issues.
Ensure metadata, documentation, and lineage are maintained to support discoverability and governance.
- Operational Readiness & Reliability
Drive CI/CD practices for data deployments, including testing and environment promotion.
Lead troubleshooting and root-cause analysis for complex pipeline failures.
Contribute to operational practices for incident handling and continuous improvement.
- Technical Mentorship & Collaboration
Mentor junior and mid-level data engineers on coding standards, design patterns, and troubleshooting.
Work with Product Managers and stakeholders to clarify requirements and data expectations.
Partner with analytics and BI teams to ensure data assets support reporting and insights.
Communicate clearly on progress, risks, and dependencies, including in technical design discussions.
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Required
- 5–8+ years of hands-on data engineering experience, including experience designing pipelines and data models in enterprise environments.
- Strong hands-on expertise in Databricks and Apache Spark for pipeline development and performance tuning.
- Strong SQL and Python development skills.
- Working knowledge of Azure-based cloud data environments.
- Solid experience with Delta Lake and lakehouse reliability patterns.
Preferred
- Experience with SODA or similar data quality and observability tooling.
- Familiarity with Atlan or equivalent catalog/metadata platforms.
- Power BI awareness, with an understanding of downstream reporting needs.
- Prior experience mentoring or informally leading other engineers.
Success Measures
- High-quality, reliable pipeline deliveries with minimal defects, including on complex designs.
- Improved stability and faster recovery from pipeline issues.
- Growing coverage of automated data quality checks across owned pipelines.
- Positive feedback from junior engineers on mentorship and technical guidance.
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