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

Description

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 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.

2. ETL & Reverse ETL Build

• Orchestration: Build and maintain complex, robust and idempotent Airflow 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.

3. 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.

4. Collaboration with Tech Lead & Architecture

• Blueprint Execution: Work closely with the Tech Lead to translate the Solution Architect’s 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.
• Orchestration: 3+ years of hands-on experience with Apache Airflow (DAG development, operators, sensors).
• Cloud Processing: Strong experience with Google Cloud Dataflow (Apache Beam), Dataproc, Spark, or similar cloud-native processing frameworks.
• Data Concepts: Deep understanding of ELT/ETL patterns, Data Lakehouse architecture, and Reverse ETL strategy.

Soft Skills & Qualifications

• Experience: 5+ years in Data Engineering.
• Mentorship: Experience mentoring junior engineers, providing constructive code reviews, and fostering a collaborative team environment.
• Pragmatism: An ability to balance code perfection with business deadlines using AI.
• Collaboration: Excellent communication skills to clearly articulate technical trade-offs to the Tech Lead and stakeholders.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience).

Description

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