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!
About the Role:
We're seeking a highly experienced and forward-thinking Data Engineering Tech Lead to spearhead our Americas analytics workstream on Google Cloud Platform (GCP). This role demands deep expertise in data warehousing, ETL, data modeling, advanced analytics, API design and implementation in agile mode. A strong preference for domain (Commerce, Supply Chain etc.) specific data knowledge and a passion for building reusable and sustainable technical designs is essential. Proficiency with Google Cloud services in general and Data Engineering services in particular is crucial.
In this role you will be reporting to Americas Data Domain & Analytics Lead. You will be working closely with other Domains and Analytics specific Product Delivery Managers, Product Owners, Operations, Security, Architecture and Data Privacy teams
Responsibilities:
Data Analytics Ownership:
Own the Data Analytics roadmap and strategy within GCP, championing reusability and sustainability. Drive the development of data solutions tailored for your analytics workstream, such as commerce and supply chain reporting.
Technical Leadership & Mentorship:
Lead and mentor data engineers, fostering a culture of reusability, maintainability, and long-term sustainability. Provide technical guidance, code reviews, and career development support.
Reusable & Sustainable Data Processing Pipelines:
Evolve our data architecture, focusing on reusable components and frameworks for data processing, exposed through well-defined APIs. Design and implement scalable data pipelines using GCP services (Big Query, Cloud Dataflow, Cloud Functions, Cloud Composer, Cloud Run, Workflows) with modularity and reusability as core principles, leveraging APIs for inter-service communication.
Modular Data Modeling & Warehousing:
Develop and maintain robust data models in Big Query for domain analytics, prioritizing modular design for reusability. Optimize data structures for performance, scalability, and cost-effectiveness, ensuring long-term maintainability and adaptability. Develop solutions that traverse through the Medallion Architecture.
Cloud Run & Containerization for Reusable APIs:
Leverage Cloud Run for deploying and managing containerized API services. Design reusable container images and deployment patterns. Implement efficient CI/CD pipelines for Cloud Run deployments, integrating seamlessly with other GCP services.
Big Query Optimization:
Continuously optimize Big Query performance for domain analytics, focusing on long-term sustainability and cost efficiency. Design and implement APIs for controlled and efficient access to Big Query data including versioning, authentication, and authorization.
Python Development for Reusable API Libraries:
Utilize Python for building reusable API libraries and frameworks for data manipulation, analysis, and access. Streamline data engineering workflows through reusable code components and well-documented APIs.
Collaboration & Communication:
Collaborate closely with stakeholders to understand their data needs and deliver sustainable Data Engineering solutions. Communicate technical concepts effectively, emphasizing the value of tech solutions for data accessibility and integration.
Innovation & Research for Data Solutions:
