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!
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What you will do**:
- Design and maintain high-performance data pipelines across LATAM on GCP.
- Lead infrastructure decisions and data architecture design.
- Implement robust CI/CD pipelines for data ingestion, transformation, and delivery.
- Optimize performance for large-scale datasets using advanced GCP capabilities.
- Ensure best practices for security, observability, and reliability.
- Support and mentor engineers to elevate code quality and architectural thinking.
- Troubleshoot and resolve performance bottlenecks and data inconsistencies.
- Establish reusable frameworks and data components across projects.




- Coordinate with analytics and product teams to translate requirements into data solutions.
- Drive platform scalability through modern data architecture principles.
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What we are looking for**:
- Advanced experience in GCP (BigQuery, Dataflow, Pub/Sub, Cloud Composer).
- Proficient in Python and SQL for pipeline development.
- Knowledge of modern architectures (Data Mesh, Lakehouse, microservices).
- Experience with orchestration and CI/CD practices in cloud-native environments.
- Ability to lead teams and align technical execution with platform strategy (Strategic technical leadership)
- Strong collaboration and problem-solving skills.
- Quality-focused and highly accountable technical leadership.
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Years of experience required**:
6-9 years in data engineering or backend development, with 4+ years in cloud-based environments.
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Tools & Technologies**:
- GCP: BigQuery, Dataflow (Apache Beam), Cloud Storage, Pub/Sub
- Python (advanced), SQL (advanced)
- Airflow / Cloud Composer
- CI/CD tools: Git, Cloud Build, Terraform
- Monitoring tools: Stackdriver, Datadog
- Docker / container orchestration experience
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Technical knowledge /**certifications**:
- GCP Professional Data Engineer certification (preferred)
- Deep understanding of distributed computing
- Strong knowledge of data architecture and performance tuning
- Streaming vs batch data architecture experience
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📌 Data Engineer Lead (Estado de México)
🏢 L'Oréal
📍 Estado de México