Senior Data Engineer - Python, SQL & AI-Augmented Engineering

Who Are We

For 115 years, LOral, the worlds leading beauty player, has devoted itself to one thing only: fulfilling the beauty aspirations of consumers around the world.

For more than a century, LOral has devoted itself solely to one business: Beauty.Presentin 150 countries across five continents and with 42 billionconsolidatedsales, L’‘Oral is the global industry leader.With37 global beauty brandsacross four divisions,LOral offersbeauty for eachcoveringall beauty categories and cateringto all beauty desires.With the acquisition of the Australian brandAsopin 2023, the Group continues to expand its portfolio through targeted acquisitions as part of its drive tocreate the future of beauty.

Today, LOral includes more than2,000 tech professionalsand is constantly growing.Beauty Tech is changing the game and leading the shifttowardsnew consumer realities andadigitaldisruption.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, LOral continues to recruitdiverse,innovative,skilledandpassionateminds in different tech domains such asData, Digital, Cloud, Cyber Security, IT Architecture, DevOps,Applicationsand Infrastructure.

A Day in the Life of Senior Data Engineer Python, SQL AI-Augmented Engineering

We are looking for a hands-on Senior Data Engineer to join our data squad and work directly with the Technical Lead to execute our data strategy. You will be responsible for the “build” part of the blueprint. This role focuses on delivering production-grade implementations, optimizing complex SQL, and building scalable ETL/Reverse ETL pipelines using Python, SQL, Airflow and Apache Beam. You will collaborate with the Tech Lead to ensure that AI coding assistants are used effectively, code is clean, and data is activated reliably via automated piplines.

In this role, You will..

1. AI-Driven Data Engineering (Execution Focus)

• AI Productivity:Use GitHub Copilot, Cursor, ChatGPT or Claude Code to generate complex SQL transformations, Python scripts, PySpark logic and data processing pipelines, in accordance with the AI standardization strategy.
• Performance Tuning:Use AI tools to analyze query execution plans, identify bottlenecks, and refactor legacy SQL for performance and cost efficiency (e.g., reducing BigQuery/Snowflake slot usage).
• Data Validation:Utilize AI to generate comprehensive test suites for data quality checks and schema drift detection.

1. ETL Reverse ETL Build

• Orchestration:Build and maintain complex, robust and idempotentAirflow DAGs. Ensure high availability and observability of schedules.
• Batch Streaming:Develop data pipelines using Python, Spark, or Google Cloud Dataflow (Apache Beam), Dataform to handle large-scale data transformations.
• Data Activation:Design and implement Reverse ETL processes to sync data between the Data Warehouse and critical business applications (Salesforce, Tealium, Braze, Google Ads etc), ensuring “Data Activation” is seamless and reliable.

1. Python SQL Excellence

• Core Libraries:Contribute to the development and maintenance of internal Python libraries and custom Airflow operators to ensure DRY (Don’‘t Repeat Yourself) principles across the team.
• SQL Governance:Write high-performance SQL code. Conduct peer reviews focused on window functions, partitioning, and indexing to maintain the “Gold Standard” set by the Tech Lead.

1. Collaboration with Tech Lead Architecture

• Blueprint Execution: Work closely with the Tech Lead to translate the Solution Architects high-level ERDs (Entity Relationship Diagram) into optimized physical tables and views.
• Operational Integrity: Refine the CI/CD strategy for data (Git, dbt, or similar) alongside the Tech Lead and DevOps to ensure smooth, zero-downtime deployments from development to production.

What are we looking for

• AI Tooling: Proven experience utilizing AI coding assistants (GitHub Copilot, Claude Code, Cursor) to accelerate development and troubleshooting.
• Data Stack: Expert-level proficiency in Python 3.x (data-focused libraries) and advanced SQL (analytical functions, recursive CTEs, query optimization), BigQuery and Dataform are a plus. -…