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 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!

YEARS OF EXPERIENCE REQUIRED:

4–6 years in data engineering, with 2+ in a team or technical leadership capacity.

TOOLS & TECHNOLOGIES:

• BigQuery (advanced use)
• SQL, Python
• dbt, Dataform for transformation
• Airflow, Cloud Composer
• CI/CD pipelines and versioning tools (Git, GitLab, etc.)
• GCS, Pub/Sub, structured & semi-structured data formats (JSON, Parquet)

Technical knowledge / certifications:

• Strong in query optimization and storage design
• Experience implementing data validation and monitoring logic
• GCP familiarity mandatory, certification is a plus

Experience defining reusable transformation logic across domains
Don’t meet every single requirement? At L’Oréal, we are dedicated to building a diverse, inclusive, and innovative workplace. If you’re excited about this role but your past experience doesn’t align perfectly with the qualifications listed in the job description, we encourage you to apply anyways! You may just be the right candidate for this or other roles!
We are an Equal Opportunity Employer and take pride in a diverse environment. We would love to find out more about you as a candidate and do not discriminate in recruitment, hiring, training, promotion, or other employment practices for reasons of race, color, religion, gender, sexual orientation, national origin, age, marital or veteran status, medical condition or disability, or any other legally protected status

• Lead the development of efficient, scalable data pipelines using GCP tools.
• Co-define architecture alongside senior data engineering leaders.
• Apply best practices in transformation logic, versioning, and performance tuning.
• Build both batch and streaming pipelines, adapting to various business needs.
• Enable monitoring, logging, and validation across the pipeline lifecycle.
• Partner with analysts to ensure datasets are accessible and optimized for consumption.
• Improve code quality and reusability across data workflows.
• Support team members with reviews and technical coaching.
• Identify opportunities to streamline or modernize the platform.
• Ensure data reliability and consistency in daily and long-term delivery.