Data Engineering Manager

Ralph Lauren; Bengaluru; On-site; INR 3,600,000 - 6,000,000; Full time

Job summary

Ralph Lauren is seeking an experienced Data Engineering Manager in Bengaluru to lead end‑to‑end data pipeline delivery, steward enterprise data products, and partner with product, platform, and governance teams. The role emphasizes execution rigor, people leadership, and scalable, secure data architectures.

You will drive data quality, lineage, and documentation while aligning with enterprise standards and cost efficiency. A strong focus on collaboration and continuous improvement is expected.

Qualifications

• 7–10 years of experience in data engineering, including team or delivery leadership.
• Experience building and operating enterprise‑scale data pipelines in complex environments.
• Strong stakeholder management across product, engineering, analytics, and governance teams.
• Ability to balance speed, quality, and stability in delivery decisions.

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Skills

Databricks & Apache Spark

Azure

Delta Lake / Lakehouse

CI/CD for data pipelines

SQL & Python

Tools

SODA

Atlan

Power BI

Job description

Overview

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.

Data Engineering Manager – Role Summary

The 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 focus is on execution, engineering rigor, team leadership, and cross‑functional coordination, while product strategy and prioritization remain with Product leadership.

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Required Skills

• 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

Preferred Skills

• SODA or equivalent data quality/observability tools
• Atlan or similar data catalog and metadata platforms
• Power BI awareness – understanding downstream reporting and consumption requirements

Qualifications

• Typically 7–10 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.