Senior Data Engineering Manager-2

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.

The Senior Data Engineering Manager is responsible for leading the delivery and operational excellence of Ralph Lauren’s enterprise data engineering capabilities that underpin Data Products, analytics, and AI enablement. This role manages a team of data engineers and partners closely with Data Product Managers, Platform teams, Governance, and Analytics to deliver reliable, scalable, secure, and well-governed data pipelines and curated datasets aligned to business priorities. The Data Engineering Manager focuses on execution, engineering rigor, team leadership, and cross-functional coordination, while product strategy and prioritization remain with Product leadership.

1. Data Engineering Delivery

• Lead end to end delivery of data pipelines and curated datasets supporting enterprise data products.

• Drive predictable execution aligned to sprint and release plans in partnership with Product and Delivery leadership.

• Establish and enforce engineering standards for pipeline design, data transformations, testing, and reusability.

• Proactively manage delivery risks, technical dependencies, and production issues.

2. Platform & Architecture Alignment

• Ensure engineering solutions align with enterprise data platform standards and lakehouse design patterns.

• Partner with platform and architecture teams to implement scalable, secure, and cost effective engineering solutions.

• Guide teams on appropriate use of shared platforms, environments, and datasets.

3. Data Quality, Governance & Trust

• Embed automated data quality checks and monitoring into pipelines as standard practice.

• Ensure metadata, lineage, and documentation requirements are met to support discoverability and governance.

• Partner with data governance and security teams to ensure compliance, auditability, and responsible data usage.

4. Engineering Excellence & Operational Readiness

• Drive CI/CD practices for data pipelines, including automated testing, deployments, and controlled promotions.

• Ensure pipelines are operationally ready with monitoring, alerting, and clear ownership for incident resolution.

• Continuously improve performance, reliability, and cost efficiency of data workloads.

5. Stakeholder & Cross Functional Collaboration

• Partner with Data Product Managers to translate product needs into executable engineering deliverables.

• Collaborate with Analytics and BI teams to ensure data assets support governed reporting and consumption.

• Communicate delivery status, risks, and trade offs clearly to stakeholders and leadership.

6. People Leadership & Team Development

• Manage, mentor, and develop a team of data engineers across experience levels.

• Set clear expectations around quality, delivery discipline, and operational ownership.

• Foster a culture of continuous improvement, documentation, and shared accountability.

Must Have (Strong hands on leadership)

• Databricks & Apache Spark - delivery leadership, troubleshooting, performance tuning

• Azure - operating within Azure based data ecosystems, identity and access concepts

• Delta Lake / Lakehouse patterns - scalable data modeling and pipeline design

• CI/CD for data pipelines - automated build, test, deploy, and release practices

• SQL & Python - strong proficiency

Good to Have

• SODA or equivalent data quality / observability tools

• Atlan or similar data catalog and metadata platforms

• Power BI awareness - understanding downstream reporting and consumption requirements

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Qualifications

• Typically more than 10 years of experience in data engineering, including team or delivery leadership roles.

• Experience building and operating enterprise scale data pipelines in complex environments.

• Strong stakeholder management skills across product, engineering, analytics, and governance teams.

• Ability to balance speed, quality, and stability in delivery decisions