Data Engineering Manager

Position Purpose and Scope:

Who we are:

The Ralph Lauren Corporation, a global leader in luxury fashion and design, has implemented ERP solutions to support its Supply chain, Finance and HR business in North America and EMEA.

Ralph Laurens India Technology group is focused on building & leveraging high-quality technology solutions to enhance business & digitization opportunities across channels and geographies.

Purpose &

• Scope:

The Data Engineering Manager is responsible for leading the delivery and operational excellence of Ralph Laurens 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.

What you will be doing (responsibilities):

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 data lineage, 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.

What you bring (Qualifications):

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

Other Qualifications:

• Typically 710 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.

Success Measures:

• Predictable delivery of data engineering commitments with reduced rework.
• Improved data pipeline stability, observability, and incident response.
• Increased data quality coverage and faster issue resolution.
• High stakeholder confidence in reliability and execution.