Director, Data Engineering Tech Lead

Job Title Director, Data Engineering Tech Lead

Division: IT, Data

Location: Hudson Yards, NYC

Reports To: AVP, IT Domain Lead Data

Who We Are:

For more than a century, L'Oreal 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 beautyin terms of quality, efficacy, safety, sincerity and responsibility to satisfy all beauty needs and desires in their infinite diversity.

At L'Oreal, 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'Oreal IT because things change at the speed of light! Come join our dynamic team!

What You Will Do:

We're seeking a highly experienced and forward-thinking Data Engineering Tech Lead to spearhead our Americas Data acquisition, standardization and harmonization initiatives on Google Cloud Platform (GCP). This role demands deep expertise in data warehousing, ETL, data modeling, BI reporting, advanced analytics, and API design and implementation. A strong preference for domain (Commerce, Supply Chain, Consumer O+O etc.) specific data knowledge and a passion for building reusable and sustainable technical designs are essential. Proficiency with Cloud Composer, Cloud Run, BigQuery, Python, and API, and Power BI development is crucial.

In this role you will be reporting to Americas Data Domain Lead. You will be working closely with other Domain specific Product Delivery Managers, Product Owners, Operations, Security and Data Privacy teams

Responsibilities:

• Data Domain Ownership: Own the Data Domain roadmap and strategy within GCP, championing reusability and sustainability. Drive the development of data solutions tailored for the domain, such as product catalogs, order processing, sellout, customer behavior analysis, and marketing campaign performance.
• 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, emphasizing API design best practices and robust data solutions.
• Reusable & Sustainable Data Architecture with APIs: 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 (BigQuery, Cloud Dataflow, Cloud Functions, Cloud Composer, Cloud Run) with modularity and reusability as core principles, leveraging APIs for inter-service communication.
• Modular Data Modeling & Warehousing: Develop and maintain robust data models in BigQuery for domain analytics, prioritizing modular design for reusability. Optimize data structures for performance, scalability, and cost-effectiveness, ensuring long-term maintainability and adaptability.
• Domain BI & Reporting: Develop interactive dashboards and reports in Power BI, integrating with APIs to access real-time and processed domain data. Focus on key domain metrics, ensuring data accuracy, consistency, and user-friendliness.
• 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.
• BigQuery Optimization & API Access: Continuously optimize BigQuery performance for domain analytics, focusing on long-term sustainability and cost efficiency. Design and implement APIs for controlled and efficient access to BigQuery 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: Stay at the forefront of data engineering and google cloud development trends, exploring new approaches to enhance reusability, sustainability, and scalability of data solutions.

What We Are Looking For:

Required Qualifications:

• 10 years of experience in data engineering, data warehousing and analytics.
• 4+ years of experience with Google Cloud Platform (GCP), including BigQuery, Cloud Dataflow, Cloud Functions, Cloud Composer, Pub/Sub, Cloud Workflows, Cloud Build and Cloud Run.
• Experience with Power BI development, including connecting to BigQuery and creating interactive dashboards.
• Proven experience in implementing BigQuery Access Controls like Row and Column Level Security
• Experience implementing Data Governance and Data Quality standards
• Experience in GCP and PowerBI performance tuning and cost optimizations
• Proven experience in data warehousing, ETL processes, and data modeling.
• Experience with containerization technologies (Docker, Kubernetes).
• Proficiency in SQL and at least one scripting language (e.g., Python).
• Excellent communication, interpersonal, and teamwork skills.
• Strong problem-solving and analytical skills.
• Experience leading and mentoring a team of engineers.
• Google Cloud Professional Data Engineer Certification.

Preferred Qualifications:

• Strong understanding of software design principles and patterns for building maintainable and scalable systems.
• Experience with infrastructure-as-code (Terraform et al) and configuration management tools.
• Experience working with large-scale domain specific datasets and understanding of data structures and business processes.

What's In It For You:

• Salary Range: $138,500-200,800 The actual compensation will depend on a variety of job-related factors which may include geographic location, work experience, education, and skill level)
• Competitive Benefit Package (Medical, Dental, Vision, 401K, Pension Plan)
• Hybrid Work Policy (3 Days in Office, 2 Days Work from Home)
• Flexible Time Off (Paid Company Holidays, Paid Vacation, Vacation Buy Program, Volunteer Time, Summer Fridays & More!)
• Access to Company Perks (VIP Access to L'Oreal's Internal Shop for Discounted Products, Monthly Mobile Allowance)
• Learning & Development Opportunities (Unlimited Access to E-learnings, Lunch & Learn Sessions, Mentorship Programs, & More!)
• Employee Resource Groups (Think Tanks and Innovation Squads)
• Access to Mental Health & Wellness Programs

Don't meet every single requirement? At L'Oreal, 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.

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