Dir - Cloud Data Engineering & AI Tech Lead

Job Title Cloud Data Engineering & AI Tech Lead

Division: Corporate IT, Data & AI

Location: New York, NY

Reports To: Data & AI - Domain Analytics Lead

Who We Are:
For more than a century, L’Oréal has devoted its energy, innovation, and scientific excellence solely to one business: Beauty. Our goal is to offer each and every person around the world the best of beauty in terms of quality, efficacy, safety, sincerity and responsibility to satisfy all beauty needs and desires in their infinite diversity.

At L’Oréal, our IT teams design and build solutions to ensure high performance for all our business sectors by imagining new ways of doing things, from designing websites to building algorithms and predicting new trends. They can be found leading teams towards a more connected and digitalized future in IT retail, e-commerce, CRM, data, AI, cybersecurity, Cloud and E-Marketing. You never stop learning at L’Oréal IT because things change at the speed of light! Come join our dynamic team!

What You Will Do:
L’Oréal is driving a massive Beauty Tech Transformation roadmap, designing and implementing next-generation Data Products to accelerate business growth across Commerce and Supply Chain.

As a Tech lead, you will design, build, and optimize scalable, secure, and reusable data products on Google Cloud Platform. You will combine strong hands-on data engineering expertise with practical AI engineering experience to make governed enterprise data available for analytics, reporting, and natural-language insight experiences.

You will work closely with Data Architects, BI Developers, Data Scientists, Product teams, and business stakeholders to build reliable data foundations, standardize metric definitions, and enable trusted data access. This role requires strong technical ownership, a hands-on delivery mindset, and the ability to mentor engineers while driving engineering standards.

What You Will Do

• Design, build, and maintain scalable, reusable, and cost-efficient data pipelines and data products on Google Cloud Platform.
• Develop resilient ETL/ELT pipelines supporting batch, near-real-time, and streaming data workloads across a modern Medallion architecture.
• Build and optimize enterprise data models, including dimensional models and Star/Snowflake schemas.
• Design and implement solutions using BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Composer, Cloud Functions, Cloud Run, Workflows, and Terraform.
• Optimize BigQuery performance, cost, reliability, and governance, including partitioning, clustering, query optimization, row-level security, and column-level security.
• Build reusable APIs, services, and engineering utilities using Python, Docker, Kubernetes, Cloud Run, and CI/CD pipelines.
• Partner with Data Architects, BI Developers, Data Scientists, and business stakeholders to translate business requirements into scalable, maintainable technical solutions.
• Support the development and optimization of governed semantic layers and standardized business metrics in Looker and other reporting environments.
• Establish and promote best practices for data quality, testing, observability, documentation, metadata, lineage, deployment, and operational support.

AI-Enabled Data Engineering

• Turn governed data into clear, natural-language answers that enable teams to explore insights safely and efficiently.
• Design, build, and optimize Skills, AI Agents, and conversational workflows that translate business queries into accurate and understandable insights.
• Develop and operationalize AI-enabled patterns such as Retrieval-Augmented Generation (RAG), Text-to-SQL, agentic workflows, and tool-based AI orchestration.
• Connect governed BigQuery datasets and the Looker semantic layer to AI-enabled solutions, ensuring consistent metric definitions and trusted business logic.
• Build robust tools, workflows, guardrails, monitoring, and evaluation frameworks to reduce hallucinations, improve answer quality, and help prevent unauthorized data exposure.
• Partner with Data Science and AI teams to productionize AI and machine learning use cases by building reliable, governed, and scalable data foundations.

Agile Delivery & Technical Leadership

• Translate broad business epics into clear, developer-ready technical stories, tasks, and acceptance criteria.
• Participate actively in Agile ceremonies, backlog refinement, sprint planning, design revi…