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About This Role
Job Title Cloud Data Engineering & AI Tech Lead
Division: Corporate IT, Data & AI
Location: New York, NY
Reports To: Data & AI - Domain Analytics Lead
Who We Are:
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 each and 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!
What You Will Do:
L’Oréal is driving a massive Beauty Tech Transformation roadmap, designing and implementing next-generation Data Products to accelerate business growth across Commerce and Supply Chain.
As a Tech lead, you will design, build, and optimize scalable, secure, and reusable data products on Google Cloud Platform. You will combine strong hands-on data engineering expertise with practical AI engineering experience to make governed enterprise data available for analytics, reporting, and natural-language insight experiences.
You will work closely with Data Architects, BI Developers, Data Scientists, Product teams, and business stakeholders to build reliable data foundations, standardize metric definitions, and enable trusted data access. This role requires strong technical ownership, a hands-on delivery mindset, and the ability to mentor engineers while driving engineering standards.
What You Will Do
- Design, build, and maintain scalable, reusable, and cost-efficient data pipelines and data products on Google Cloud Platform.
- Develop resilient ETL/ELT pipelines supporting batch, near-real-time, and streaming data workloads across a modern Medallion architecture.
- Build and optimize enterprise data models, including dimensional models and Star/Snowflake schemas.
- Design and implement solutions using BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Composer, Cloud Functions, Cloud Run, Workflows, and Terraform.
- Optimize BigQuery performance, cost, reliability, and governance, including partitioning, clustering, query optimization, row-level security, and column-level security.
- Build reusable APIs, services, and engineering utilities using Python, Docker, Kubernetes, Cloud Run, and CI/CD pipelines.
- Partner with Data Architects, BI Developers, Data Scientists, and business stakeholders to translate business requirements into scalable, maintainable technical solutions.
- Support the development and optimization of governed semantic layers and standardized business metrics in Looker and other reporting environments.
- Establish and promote best practices for data quality, testing, observability, documentation, metadata, lineage, deployment, and operational support.
AI-Enabled Data Engineering
- Turn governed data into clear, natural-language answers that enable teams to explore insights safely and efficiently.
- Design, build, and optimize Skills, AI Agents, and conversational workflows that translate business queries into accurate and understandable insights.
- Develop and operationalize AI-enabled patterns such as Retrieval-Augmented Generation (RAG), Text-to-SQL, agentic workflows, and tool-based AI orchestration.
- Connect governed BigQuery datasets and the Looker semantic layer to AI-enabled solutions, ensuring consistent metric definitions and trusted business logic.
- Build robust tools, workflows, guardrails, monitoring, and evaluation frameworks to reduce hallucinations, improve answer quality, and help prevent unauthorized data exposure.
- Partner with Data Science and AI teams to productionize AI and machine learning use cases by building reliable, governed, and scalable data foundations.
Agile Delivery & Technical Leadership
- Translate broad business epics into clear, developer-ready technical stories, tasks, and acceptance criteria.
- Participate actively in Agile ceremonies, backlog refinement, sprint planning, design reviews, and iterative delivery.
- Provide technical leadership through architecture reviews, engineering standards, code reviews, and delivery guidance.
- Mentor Data Engineers and partner teams, supporting their technical growth and consistent adoption of engineering best practices.
What We Are Looking For:
Required Qualifications:
- 7+ years of experience in data engineering, data architecture, analytics engineering, or related technical roles.
- 4+ years of hands-on GCP experience, designing and implementing production-grade data solutions.
- Strong hands-on expertise in:
- BigQuery
- SQL
- Python
- Dataflow and/or Dataproc
- Pub/Sub
- Cloud Composer
- Cloud Run and/or Cloud Functions
- Terraform and CI/CD toolchains
- Strong experience designing and optimizing enterprise data models, ETL/ELT pipelines, and data platforms for structured, semi-structured, and unstructured data.
- Proven experience with BigQuery performance tuning, partitioning, clustering, cost optimization, and secure data-access controls.
- Strong proficiency in Looker semantic modeling, including governed metrics, explores, reusable dimensions, and consistent business definitions.
- Demonstrated hands-on experience implementing AI-enabled data solutions, such as RAG, AI agents, Text-to-SQL, LLM-powered analytics, or conversational data experiences.
- Experience developing secure and scalable APIs or services using Python and containerization technologies such as Docker and Kubernetes.
- Experience working in Agile delivery environments and translating business needs into actionable technical requirements.
- Strong communication, collaboration, and technical problem-solving skills.
- Work authorization on a continuous, ongoing basis. Sponsorship is not offered for this role.
Preferred Qualifications:
- Strongly preferred: Experience working with Commerce data domains, including related data structures, metrics, reporting, and operational processes.
- Experience with Power BI, DAX, Tableau, Looker Studio, or other enterprise BI and visualization platforms.
- Experience with AI evaluation, prompt engineering, LLM observability, model safety controls, or responsible AI data-governance patterns.
- Google Cloud Professional Data Engineer certification.
- Exposure to ReactJS or development of lightweight internal web applications.
As an integral part of our culture at L’Oréal, wellness is at our core. We not only offer a generous Benefits Package (Medical, Dental, Vision, 401K), we also offer flexible time off, work from home days, and a pension plan! Additionally, you will have access to company perks such as Makeup, Skincare, and Fragrance! Employees receive a stipend for L'Oréal products as well as VIP Access to L’Oréal’s Internal Shop for Discounted Products, and other perks such as Monthly Mobile Allowance, discounted gym memberships, and ERGs.
Additional Benefits Information As Follows:
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); Being present in the office three days a week is considered an essential function of all office-based roles for eligible full-time employees.
- Flexible Time Off (Paid Company Holidays, Paid Vacation, Vacation Buy Program, Volunteer Time, Summer Fridays & More!)
- Access to Company Perks (VIP Access to L’Oréal’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'Oréal, 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.
If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to access job openings or apply for a job on this site as a result of your disability. You can request reasonable accommodations by contacting [1] [email protected]. If you need assistance to accommodate a disability, you may request an accommodation at any time.
Our Safe Together Plan: Your safety is our highest priority. We will proceed with caution and adhere to enhanced protection standards to ensure our sites are safe for all employees. We must all operate with the shared responsibility for each other’s health & safety in mind.
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Salary Context
This $138K-$200K range is above the 75th percentile for Data Engineer roles in our dataset (median: $145K across 47 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 11,520 AI roles we're tracking, Data Engineer positions make up 1% of the market. At L'Oréal, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $177,000 based on 195 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $138K to $200K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
L'Oréal AI Hiring
L'Oréal has 2 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer. Positions span New York, NY, US, New York, NY. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national median.
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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