Manager - Data Engineer

Purpose & Scope:

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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 .

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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 wellgoverned data pipelines and curated datasets aligned to business priorities.

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The Data Engineering Manager focuses on execution, engineering rigor, team leadership, and crossfunctional coordination , while product strategy and prioritization remain with Product leadership.

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What you will be doing (responsibilities):
\n 1. Data Engineering Delivery
\n • Lead end to end delivery of data pipelines and curated datasets supporting enterprise data products.
\n • Drive predictable execution aligned to sprint and release plans in partnership with Product and Delivery leadership.
\n • Establish and enforce engineering standards for pipeline design, data transformations, testing, and reusability.
\n • Proactively manage delivery risks, technical dependencies, and production issues.
2. Platform & Architecture Alignment
\n • Ensure engineering solutions align with enterprise data platform standards and lakehouse design patterns.
\n • Partner with platform and architecture teams to implement scalable, secure, and cost effective engineering solutions.
\n • Guide teams on appropriate use of shared platforms, environments, and datasets.
3. Data Quality, Governance & Trust
\n • Embed automated data quality checks and monitoring into pipelines as standard practice.
\n • Ensure metadata, lineage, and documentation requirements are met to support discoverability and governance.
\n • Partner with data governance and security teams to ensure compliance, auditability, and responsible data usage.
4. Engineering Excellence & Operational Readiness
\n • Drive CI/CD practices for data pipelines, including automated testing, deployments, and controlled promotions.
\n • Ensure pipelines are operationally ready with monitoring, alerting, and clear ownership for incident resolution.
\n • Continuously improve performance, reliability, and cost efficiency of data workloads.
5. Stakeholder & Cross Functional Collaboration
\n • Partner with Data Product Managers to translate product needs into executable engineering deliverables.
\n • Collaborate with Analytics and BI teams to ensure data assets support governed reporting and consumption.
\n • Communicate delivery status, risks, and trade offs clearly to stakeholders and leadership.
6. People Leadership & Team Development
\n • Manage, mentor, and develop a team of data engineers across experience levels.
\n • Set clear expectations around quality, delivery discipline, and operational ownership.
\n • Foster a culture of continuous improvement, documentation, and shared accountability.

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What you bring (Qualifications):
Qualifications
Must Have (Strong hands on leadership)
\n • Databricks & Apache Spark – delivery leadership, troubleshooting, performance tuning
\n • Azure – operating within Azure based data ecosystems, identity and access concepts
\n • Delta Lake / Lakehouse patterns – scalable data modeling and pipeline design
\n • CI/CD for data pipelines – automated build, test, deploy, and release practices
\n • SQL & Python – strong proficiency

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

Qualifications
\n • Typically 12+ years of experience in data engineering, including team or delivery leadership roles.
\n • Experience building and operating enterprise scale data pipelines in complex environments.
\n • Strong stakeholder management skills across product, engineering, analytics, and governance teams.
\n • Ability to balance speed, quality, and stability in delivery decisions.

Success Measures
\n • Predictable delivery of data engineering commitments with reduced rework.
\n • Improved data pipeline stability, observability, and incident response.
\n • Increased data quality coverage and faster issue resolution.
\n • High stakeholder confidence in reliability and execution.
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. Skillset Required: Azure Databricks, CICD , Python, SQL, Apache Spark, Delta lake, Lakehouse Pattern, Team lead, Lead Data Engineer