IT Analytics Lead, Data Engineering & Delivery

Ref #:

W166402

Department:

Data Analytics

City:

Bangalore

State/Province:

Karnataka

Location:

India

Pay Range Max

Pay Range Min

Company Description

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.

Position Overview

The Senior Director of Data & AI Engineering will lead Ralph Lauren’s enterprise-wide data and AI transformation through the RL Data Strategy initiative. This executive technical role defines and delivers our next-generation Data & AI platform, accelerates AI-enabled business capabilities, and establishes Ralph Lauren as a data-first, AI-augmented organization.

As a key member of the Technology leadership team, this role will:

• Set the architectural vision and drive execution for a unified, cloud-native Data & AI platform (data lakehouse, streaming, semantic layer, feature store, model serving, and LLMOps).

• Modernize the data estate to support global scale, real-time decisioning, and AI workloads with strong security, compliance, and cost efficiency.

• Enable advanced analytics, ML, and generative AI across digital commerce, retail stores, supply chain, merchandising, finance, and customer engagement.

• Champion engineering excellence—from DataOps and MLOps to LLMOps—embedding reliability, observability, automation, and responsible AI throughout.

This leader will architect, build, and optimize enterprise-scale data products and AI platforms that deliver governed, performant, and interoperable data at global scale. They will establish technical standards, drive best practices, and ensure seamless integration across technology and business domains. The role requires a strategic mindset—balancing immediate business outcomes with a long-term technical vision—and deft stakeholder management across Technology, Analytics, and Business leadership.

The position reports to the Head, Global Data & Analytics and partners closely with senior leaders in Technology, Data Governance, Enterprise Architecture, Digital, and Business functions to turn data and AI into enduring competitive advantage.

Essential Duties & Responsibilities

Key Responsibilities

Platform & Architecture

• Architect and deliver an enterprise-scale Data & AI platform (lakehouse, streaming, semantic layer, feature store, model registry, vector search, model serving) enabling predictive analytics and generative AI at global scale.

• Unify and modernize the data estate, consolidating legacy BI/data systems into cloud-native, AI-optimized foundations (batch + streaming) with strong security, data privacy, and regional data residency.

• Define cross-domain semantic models and interoperability standards that power governed self-service analytics and consistent model features across domains.

Data Engineering & Productization

• Lead the design of intelligent data pipelines for real-time ingestion, transformation, feature engineering, and RAG (retrieval augmented generation) use cases; ensure SLAs for latency, freshness, and quality.

• Build reusable, governed data products (conformed data sets, feature stores, vector indexes) that serve merchandising, supply chain, finance, digital commerce, marketing, and retail operations.

• Institutionalize DataOps (CI/CD for data, automated testing, data contracts, schema evolution, lineage, cataloging) to increase reliability and speed-to-value.

AI Engineering, MLOps & LLMOps

• Establish MLOps and LLMOps foundations (model registry, automated training/validation, deployment, monitoring, rollback, A/B testing, prompt and policy management).

• Partner with Data Science and AI teams to define feature stores, model serving architectures (online/offline), and scalable inference patterns (batch, real-time, edge).

• Operationalize generative AI responsibly: secure retrieval pipelines, grounding to governed data, red-teaming, content filters, safety/evaluation harnesses, and human-in-the-loop review.

Reliability, Observability & Cost Optimization

• Embed SRE practices for data and AI systems—comprehensive observability (metrics, logs, traces), data quality monitors, drift detection, hallucination/guardrail alerts.

• Optimize performance and cost across storage, compute, and inference; drive FinOps for data/AI (unit economics per domain, capacity planning, autoscaling).

Governance & Responsible AI

• Co-lead data governance with the Data Governance Office—data policies, access controls, privacy-by-design, PII handling, retention, data contracts, and stewardship.

• Champion responsible AI—bias detection and mitigation, explainability, model risk management, compliance with applicable regulations and internal standards.

Leadership, Operating Model & Culture

• Build and mentor a high-performing global team (Data Platform Engineering, Data Product Engineering, ML Platform/AI Engineering, DataOps/MLOps, Observability).

• Adopt a product operating model—prioritize backlogs with business owners, define roadmaps and OKRs, and measure outcomes (not just outputs).

• Drive change management—enable self-service, uplift engineering practices, and scale data literacy for business stakeholders.

Experience, Skills & Knowledge

Qualifications

• 10–15+ years in data engineering, data architecture, or related fields; 7+ years leading large, multi-disciplinary engineering teams.

• Proven success delivering cloud-based data platforms (Azure, AWS, or GCP) and distributed processing/orchestration (e.g., Spark, Databricks/Snowflake/Synapse/BigQuery; Airflow/Azure Data Factory/dbt/Kubernetes).

• Strong expertise in dimensional and semantic modeling, data product design, and feature store patterns for ML at enterprise scale.

• Experience operationalizing MLOps/LLMOps (e.g., MLflow/Kubeflow/SageMaker; vector databases such as Pinecone/Weaviate/OpenSearch/CosmosDB; RAG pipelines).

• Deep understanding of streaming architectures (Kafka/Event Hub/Kinesis), real-time model serving, and scalable inference.

• Track record of delivering performant, governed, interoperable data and AI solutions with measurable business outcomes.

• Excellent leadership, communication, vendor management, and cross-functional partnership skills; executive presence and stakeholder influence.

• Preferred: Retail/commerce/supply chain analytics experience; FinOps for data/AI; privacy-by-design and model risk management.

Leadership & Operating Model

• Team Structure: Data Platform, Data Product Engineering, AI/ML Platform, DataOps/MLOps, Observability & SRE, and Governance partners.

• Ways of Working: Agile product squads aligned to business domains; shared platform capabilities; federated data stewardship; centralized AI safety standards.

• Partnerships: Data Governance, Enterprise Architecture, Cybersecurity, Digital Product, Supply Chain, Finance, Merchandising, and Store Ops.

Ralph Lauren

Career Opportunity

IT Analytics Lead, Data Engineering & Delivery

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