Data Engineer – L’Oréal (Beauty Tech)

Opportunity Overview

Attribute Details
Organisation L’Oréal (Beauty Tech Division)
Position Title Data Engineer
Experience Requirement 4 – 6 Years in Data Engineering with mandatory GCP experience
Primary Location Hyderabad, Telangana, India
Work Model Hybrid Setup (3 Days Office / 2 Days Work From Home)
Department IT, Data & Beauty Tech / Global Data Platform Team
Employment Type Full-Time, Permanent
Target Sector Personal Care, Cosmetics & Beauty Tech eCommerce
Academic Target Bachelor’s or Master’s degree in CS, IT, Engineering, or related quantitative field
Core Technical Stack GCP Ecosystem (BigQuery, GCS, Dataflow, Dataproc, Pub/Sub, Cloud Composer), Apache Spark, Python, SQL, Git, DataOps/CI-CD

Beauty Tech Data Architecture & Pipeline Lifecycle

The Data Engineer at L’Oréal designs scalable cloud-native architectures that consolidate global consumer touchpoints, retail streams, and enterprise systems to power reporting, AI, and personalised beauty experiences:

┌───────────────────────────────────────────┐
│ 1. Enterprise Data Ingestion & Streaming │ ➔ Stream event data via Pub/Sub and ingest API/enterprise feeds into Cloud Storage
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 2. Scalable ETL/ELT & Spark Processing    │ ➔ Execute distributed transformations via Dataproc (Spark), Dataflow & Python
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 3. Lakehouse Modeling & Governance       │ ➔ Structure datasets in BigQuery using advanced data modeling, quality & security guards
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 4. DataOps & Cross-Functional Delivery   │ ➔ Automate via Cloud Composer & CI/CD pipelines to feed global BI, AI/ML & analytics
└───────────────────────────────────────────┘

Key Responsibilities

  • Cloud Platform & Pipeline Engineering: Architect, build, and optimize scalable batch and real-time ETL/ELT pipelines using GCP native tools (BigQuery, Dataflow, Dataproc, Pub/Sub).

  • Enterprise Integration: Ingest and unify structured and unstructured data from multi-channel enterprise systems, retail platforms, third-party APIs, and streaming sources.

  • Modern Data Lakehouse Architecture: Design and maintain cloud data lakes and data warehouses, enforcing advanced SQL data modeling practices and schema optimization.

  • DataOps & CI/CD Automation: Implement DevOps and DataOps best practices, including Git version control, continuous integration/continuous delivery (CI/CD), automated testing, and pipeline monitoring.

  • Governance & AI Support: Enforce strict data quality, metadata management, and data security protocols while serving structured datasets to global analytics, BI, and AI/ML teams.

Qualification Matrix & Technical Skill Stack

Core Requirements

Category Specifications
Educational Background Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or quantitative fields.
Professional Experience 4 to 6 years as a Data Engineer with mandatory, deep hands-on expertise on Google Cloud Platform (GCP).
GCP Core Services Expertise across BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, and Cloud Composer.
Programming & Processing Advanced Python scripting, complex SQL data modeling, and Apache Spark distributed computing.
Orchestration & DevOps Experience with Apache Airflow / Cloud Composer, CI/CD automation, Git, and Agile methodologies.
Domain Competencies Strong understanding of enterprise data governance, security, and global stakeholder communication.

Recruitment Process & Interview Preparation

Selection Stages

  1. HR Screening: Culture fit, background evaluation, and hybrid work expectation alignment.

  2. Technical Interview: Deep dive into GCP architecture, SQL/Python live coding, Spark performance tuning, and DataOps principles.

  3. Hiring Manager Interview: High-level system design, global collaboration scenarios, and strategic Beauty Tech alignment.

Key Focus Areas

  1. GCP Data Lakehouse Design: Practice designing end-to-end GCP architectures that process both streaming (Pub/Sub + Dataflow) and batch (Dataproc + BigQuery) datasets.

  2. BigQuery & Spark Optimization: Review partition/clustering strategies in BigQuery, along with Spark memory management and execution optimization techniques.

  3. DataOps & Airflow Orchestration: Be prepared to discuss building production DAGs in Cloud Composer, managing CI/CD pipelines, and maintaining data quality controls.

To apply for this job please visit remotejobhiring.com.