Technical Lead – Python, SQL & AI-Augmented Engineering

Unleash Your Potential at L'Oréal's Beauty Tech!

Who Are We? For 115 years, L’Oréal, the world’s leading beauty player, has devoted itself to one thing only: fulfilling the beauty aspirations of consumers around the world. For more than a century, L’Oréal has devoted itself solely to one business: Beauty. Present in 150 countries across five continents and with €42 billion consolidated sales, L'Oréal is the global industry leader. With 37 global beauty brands across four divisions, L’Oréal offers beauty for each covering all beauty categories and catering to all beauty desires. With the acquisition of the Australian brand Aēsop in 2023, the Group continues to expand its portfolio through targeted acquisitions as part of its drive to create the future of beauty. Today, L’Oréal includes more than 2,000 tech professionals and is constantly growing. Beauty Tech is changing the game and leading the shift towards new consumer realities and a digital disruption. Championing Beauty Tech, we invent the beauty of the future while becoming the company of the future. Beauty Tech is how we know our consumers intimately, augmenting their beauty journeys with unparalleled diverse and sustainable experiences. Beauty Tech equips the Group with the key assets it needs to conquer this new world, where Tech has become strategic. With this ambition, L’Oréal continues to recruit diverse, innovative, skilled and passionate minds in different tech domains such as Data, Digital, Cloud, Cyber Security, IT Architecture, DevOps, Applications and Infrastructure.

A Day in the Life of Technical Lead – Python, SQL & AI-Augmented Engineering As the Data Technical Lead, you are the

hands-on master of the data factory . You will translate the Solution Architect’s high-level data strategy into robust, production-grade code. Your mission is to lead a squad of data engineers in building scalable ETL and Reverse ETL pipelines using

Python, SQL, and Airflow , while

mandating the use of AI coding assistants to automate pipeline generation and query optimization.

In this role, You will..

• AI-Driven Data Engineering (Mandatory)
• AI Velocity: Standardize the use of GitHub Copilot, Cursor, or ChatGPT to generate complex SQL transformations, Python scripts, and PySpark logic.
• Query Optimization: Use AI-assisted tools to refactor legacy SQL for performance and cost efficiency (e.g., reducing BigQuery/Snowflake compute costs).
• Unit Testing for Data: Leverage AI to generate exhaustive test suites for data validation and schema drift detection.
• ETL & Reverse ETL Mastery
• Orchestration: Design and maintain complex, idempotent Airflow DAGs. Ensure high availability and observability of schedules.
• Streaming & Batch: Implement data processing using Google Cloud Dataflow (Apache Beam) or Spark for massive-scale transformations.
• Reverse ETL: Bridge the gap between the Data Warehouse and Business Apps (Salesforce, Braze, Google Ads) to ensure "Data Activation" is seamless and reliable.
• Python & SQL Excellence (The "How")
• Pythonic Patterns: Develop custom Python libraries and operators for the data team to ensure DRY (Don't Repeat Yourself) principles across all pipelines.
• SQL Governance: Own the "Gold Standard" for SQL performance. Conduct rigorous code reviews focusing on window functions, partitioning, and indexing.
• Partnership with Solution Architect
• From Blueprint to Build: Take the Architect's ERD (Entity Relationship Diagram) and turn it into optimized Physical Tables and views.
• Operational Integrity: Implement the CI/CD strategy for data (Git, dbt, or similar) to move from development to production without downtime.

What are we looking for? Technical Requirements

• AI Tooling: Expert proficiency in GitHub Copilot or Claude Code for accelerating Python and SQL development.
• Data Stack: Expert knowledge of Python 3.x and advanced SQL (analytical functions, recursive CTEs).
• Orchestration: 3+ years of experience with Apache Airflow.
• Cloud Processing: Proven experience with Google Cloud Dataflow (Apache Beam) or similar managed services (Dataproc, AWS Glue).
• Data Concepts: Expert in ELT/ETL patterns, Data Lakehouse architecture, and Reverse ETL strategies.

Soft Skills & Qualifications

• Experience: 7+ years in Data Engineering, with 2+ years leading a squad.
• Pragmatism: Ability to balance "Perfect Code" with "Business Velocity" using AI.
• Leadership: Experience mentoring junior data engineers in writing clean, scalable Python.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

What’s In It for You?

• Working with cutting edge Technology, empowering employees with new age learning, global exposure, and opportunities to build future-ready careers.
• A flexible and modern workplace, enabling teams to perform at their best through a smart hybrid model that supports balance and autonomy. A 3 Day in Office, 2 Day Work from Home setup.
• Employee support at every life stage, with inclusive and progressive parental policies that help individuals and families thrive.
• Holistic wellbeing offerings - personalized health benefits and strong mental wellness support to ensure employees feel their best.
• Reward and Recognition opportunities, long-term incentives, and opportunities to share in L’Oréal’s collective success.
• L'Oreal is an Equal Opportunity Employer and takes pride in a diverse environment We would love to find out more about you as a candidate and we do not discriminate in recruitment, hiring, training, promotion, or other employment practices. The beauty we find in our differences gives us the freedom to go beyond. That’s the beauty of L’Oréal.