The Business of Fashion
Website:
businessoffashion.com
Job details:
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
Ralph Lauren Consumer Technology is seeking a Test Architect - Quality Engineering, Data & AI Validation to define and scale quality engineering practices for digital commerce, data platforms, customer data initiatives, and AI-enabled solutions. This role will have a strong foundation in quality data engineering, with a focus on validating data pipelines, data lake platforms, CDP/CRM logic, migration programs, golden data sets, data lineage, and data quality controls that support enterprise AI and digital product use cases. The architect will act as a hands-on technical leader and trusted quality advisor, partnering with Product, Engineering, Data Engineering, CRM, CDP, DevOps, Security, and vendor teams to improve data quality, release confidence, automation reliability, and AI solution readiness. This role will be responsible for building reusable data validation frameworks, defining test data strategies for LLM and AI products, reviewing data logic and automation code, and embedding quality gates across data, API, integration, and digital delivery pipelines. This role will continue to support automation framework design, PR review standards, CI/CD quality gates, performance validation, and delivery governance and bring AI as a primary tool to achieve this, but the primary foundation will be data quality engineering to enable reliable AI, analytics, personalization, CRM, CDP, migration, and customer data platform initiatives.
Essential Duties & Responsibilities
- Design, build, and maintain automated validation frameworks for ETL/ELT pipelines, large-scale data systems, data integrations, APIs, and downstream data consumers.
- Define and lead the quality engineering strategy for data platforms, Data Lake, CDP, CRM, migration, integration, and AI-enabled data products across Consumer Technology.
- Collaborate with Data Engineering and Product teams to define testable data requirements, acceptance criteria, edge cases, data risk areas, and quality gates for data-oriented initiatives.
- Build reusable automation utilities using Python or related scripting languages for data validation, reconciliation, pipeline testing, API validation, and AI test data generation.
- Design and maintain golden data sets to validate LLM-based products, AI recommendations, personalization behavior, customer data logic, and deterministic/non-deterministic AI outputs.
- Define and build AI-assisted quality engineering practices that improve test design, automation development, PR review, defect analysis, and release readiness across Consumer Technology.
- Evaluate and build and own emerging AI-powered testing tools and engineering assistants that can improve framework development, automation stability, test coverage, defect triage, and developer productivity.
- Ability to define and build performance benchmarks, analyze bottlenecks, and integrate performance checks into CI/CD quality gates
- Lead automation framework modernization using tools such as Playwright, Selenium, Cypress, RestAssured, Postman, PyTest, JavaScript/TypeScript, Python, and Java based on platform needs.
- Mentor QE engineers and SDETs on framework design, coding practices, API/UI automation strategy, CI/CD integration, debugging, root cause analysis, and quality mindset.
- Promote shift-left and shift-right quality practices, including early test design, contract validation, observability-driven testing, production monitoring inputs, and feedback loops into delivery teams.
Required Experience
Experience, Skills & Knowledge
- Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or equivalent practical experience.
- 10+ years of progressive experience in quality engineering, automation engineering, SDET, test architecture, or software engineering roles, with hands-on framework ownership.
- Proven experience in building automation testing for Data Lake, CDP, CRM, migration initiatives, and large-scale data platforms.
- Strong understanding of ETL/ELT processes, data integration patterns, data transformation logic, reconciliation, schema validation, and source-to-target testing.
- Hands-on experience with cloud data platforms such as Databricks, Snowflake, Data Lake, cloud storage, and modern data pipeline orchestration tools.
- Proven ability to evaluate, design, build, and operationalize AI-powered testing tools and engineering assistants that enhance framework development, automation reliability, test coverage analysis, defect triage, root-cause insights, and developer productivity.
- Proficiency in Python, SQL, or related scripting languages for data validation, test automation, reconciliation, and quality framework development.
- Experience designing golden data sets, test data strategies, and data validation approaches for AI, LLM, personalization, recommendation, CRM, or customer intelligence products.
- Demonstrated experience designing and implementing automation frameworks for large-scale web, API, microservices, and cloud-native platforms.
- Strong ability to critically review AI-generated outputs and ensure they meet automation framework standards, maintainability expectations, security guidelines, and enterprise quality practices.
- Good knowledge of Playwright and/or Selenium/Cypress, API, Appium automation using RestAssured/Postman/Requests, and programming with JavaScript/TypeScript, Python, or Java.
- Strong understanding of framework design patterns, test data management, environment configuration, reporting, logging, code organization, and maintainable automation architecture.
- Experience integrating test automation into CI/CD pipelines such as Jenkins, Bitbucket Pipelines, GitHub Actions, GitLab CI, or Azure DevOps.
- Experience implementing quality gates, release readiness checks, smoke/regression strategies, deployment verification, and automation reporting for agile delivery teams.
- Good understanding of cloud platforms, containers, microservices, APIs, observability, and modern engineering practices.
- Strong analytical, debugging, problem-solving, communication, stakeholder management, and technical leadership skills. Preferred Skills
- Experience defining and scaling Data Quality Engineering practices, including data profiling, quality rule frameworks, lineage validation, reconciliation, anomaly detection, and quality observability for enterprise data platforms, CDP/CRM initiatives, migrations, and AI-ready datasets.
- Exposure to AI/LLM quality validation, including prompt test data, retrieval validation, golden answer sets, model output evaluation, and AI data readiness checks.
- Exposure to AI-driven testing approaches such as test generation, PR review assistance, failure analysis, self-healing automation, or intelligent test selection.
- Understanding ML and LLM fundamentals (training vs inference, embeddings, RAG, probabilistic outputs).
- Experience testing AI models and LLM-based applications using behavioral and statistical validation techniques.
- Experience with eCommerce, retail, omnichannel, personalization, search, checkout, order management, or customer-facing digital platforms.
- Ability to represent quality architecture decisions clearly to engineering leadership, product teams, vendors, and executive stakeholders.
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