LOreal is Hiring For Data Engineer Tech Lead

Loreal India; Hyderabad; Hybrid; INR 3,500,000 - 6,500,000; Full time

Job summary

L’Oréal India in Hyderabad seeks an senior Data Architect / Lead Data Engineer to own and guide end-to-end data pipeline architecture on a GCP-based platform. You will shape ingestion, transformation, and orchestration, ensuring scalable, secure, and cost-efficient data services.

You will lead Git strategy, documentation, and architectural standards while collaborating with analytics, BI, and business stakeholders to drive impactful data programs.

Qualifications

• 8+ years designing and implementing modern data engineering solutions, with at least 5 years in a technical lead or architect role
• Strong expertise in GCP data services including BigQuery, Dataflow, Pub/Sub, Airflow/Cloud Composer
• Deep SQL expertise with complex transformations and performance tuning
• Advanced data modeling including star/snowflake schemas, data vault, and semantic layers
• Experience architecting and governing large-scale ETL/ELT pipelines (batch and streaming)
• Hands-on data transformation with dbt or equivalent
• Data quality frameworks: validation, monitoring, and incident management
• Git workflows, PR reviews, and governance across a data engineering team
• CI/CD pipelines, IaC (Terraform), and automated testing for data pipelines

Responsibilities

• Guide overall data pipeline architecture leveraging GCP for scalable platforms
• Lead and govern Git strategy and repository standards
• Own technical documentation across data engineering products and architecture decisions
• Define and evolve architecture for ingestion, transformation, orchestration, and storage
• Drive pipeline performance, scalability, reliability, security, and cost-efficiency
• Collaborate with analytics engineers and BI teams to ensure data models support downstream needs
• Promote DevOps practices including automated testing and continuous deployment
• Prototype new data ingestion/transformation approaches and scale to production
• Stay updated on emerging GCP data engineering trends and best practices
• Operate within L’Oréal's data security and architectural guidelines

Skills

Solution Design

Cloud Data Platform

Deep SQL Expertise

Advanced Data Modeling

Data Pipeline

Data Transformation

Data Quality

Version Control (Git)

DevOps/Infra

Tools

BigQuery

Dataflow

Pub/Sub

Cloud Composer/Airflow

Cloud Storage

Cloud Functions

Terraform

dbt

Job description

Preferred profile/ skills:

Must Have (Core Competencies):

• Solution Design: 8+ years designing and implementing modern data engineering solutions, with at least 5 years in a technical lead or architect role for data platform/pipeline products
• Cloud Data Platform: Strong expertise in GCP data services BigQuery (architecture, partitioning, clustering, cost optimization), Dataflow, Pub/Sub, Cloud Composer/Airflow, Cloud Storage, Cloud Functions, Terraform
• Deep SQL Expertise: Complex transformations, performance tuning, query optimization at scale
• Advanced Data Modeling: Dimensional modeling, star/snowflake schemas, data marts, data vault, and designing reusable semantic/consumption layers
• Data Pipeline: Proven experience architecting and governing large-scale ETL/ELT pipelines, including batch and streaming data processing
• Data Transformation: Hands-on experience with dbt (or equivalent) for transformation frameworks, including modularization and testing best practices
• Data Quality: Experience defining and enforcing data quality frameworks validation, reconciliation, monitoring, and incident management at scale
• Version Control (Git): Experience defining and managing Git workflows, reviewing PRs, and ensuring code quality governance across a data engineering team
• DevOps/Infra: Experience with CI/CD pipelines (Cloud Build), Infrastructure as Code (Terraform), and automated testing for data pipelines Good to Have (Advantages):
• Access Control: Solid understanding of data security, access management, and architectural integrity within enterprise data platform frameworks (e.g. BTDP: Beauty Tech Data Platform)
• Certifiation: Google Cloud Professional Data Engineer / Cloud Architect certification is a plus
• Cross-domain/function Communicator: Proven ability to work cross-functionally with data analysts, analytics engineers, BI developers, and business stakeholders; and strong communication, documentation, and stakeholder management skills
• Business Context: Experience delivering at least 2 advanced data platform/analytics programs in the CPG/FMCG space

Job objectives:

Guide Overall Design: Act as a trusted advisor and subject matter expert, guiding engineering teams on data pipeline architecture and design, leveraging GCP for scalable data platforms

• Lead and Govern the Git Strategy: Defining folder structures, branching conventions, PR review processes, and repository standards
• Manage Documentation: Own technical documentation across data engineering products - architectural diagrams, design decisions, and implementation guides
• Define and Evolve Architecture: For data ingestion, transformation, orchestration, and storage aligned to enterprise standards; also establish and maintain architectural guidelines, standards, and best practices for the data engineering function
• Drive Pipeline Enhancement: Lead team members to improve performance, scalability, reliability, security, and cost efficiency across data pipelines and platforms

Job description:

Design Pipeline: Design and own end-to-end data pipeline architecture leveraging GCP data services, ensuring scalability, performance, and cost efficiency

• Guide to Orchestrate Data Workflows: Collaborate with data engineers to design and review scalable ETL/ELT pipelines and orchestration workflows (Cloud Composer/Airflow)
• Support Downstream Development: Partner with analytics engineers and BI teams to ensure data models support downstream reporting and analytics needs
• Ensure Compliance: Review code for architecture compliance, performance, and maintainability
• Contribute Operations: Promote best practices for DevOps integration, automated testing, and continuous deployment of data pipelines; and collaborate with program managers to balance new pipeline development with technical debt reduction and platform refactoring
• Rapid prototyping: Build POCs for new data ingestion/transformation approaches with a path to scale into production
• Continuous evolvement: Stay abreast of emerging technologies and evolving best practices in GCP data engineering to continuously modernize the platform
• Framework Adherence: Operate within the BTDP framework, ensuring all developments meets L'Oral's global standards for data security, access management, and architectural integrity.