Job Title:
Location:
Experience:
About MARS Cosmetics:
MARS Cosmetics is a globally recognized beauty brand that thrives on innovation, diversity, and empowering individuals through high-quality beauty solutions. Headquartered in New Delhi, MARS Cosmetics is committed to offering groundbreaking products that meet the ever-evolving needs of consumers worldwide.
About the Role:
Data Engineer
You will be responsible for integrating data from multiple business systems, developing reliable data pipelines, structuring datasets and ensuring seamless data availability for automated analytics and AI applications.
Analytics Lead – Data, AI & Automation
Key Responsibilities:
1. Data Integration & Engineering
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Build and maintain automated data pipelines connecting ERP, marketplaces, Shopify, distributor systems, marketing platforms and internal applications.
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Develop integrations using Python, SQL, REST APIs, webhooks and database connections.
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Design efficient ETL/ELT processes for data extraction, transformation and storage.
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Implement incremental data synchronisation, error handling and automated monitoring.
2. Data Architecture & Management
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Design and maintain structured databases, data warehouses and analytical datasets.
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Clean, standardise and reconcile data across multiple sources.
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Ensure data accuracy, consistency, accessibility and reliability.
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Develop reusable data models to support business analytics, reporting and AI applications.
3. Python Automation & Analytics Enablement
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Write Python and SQL scripts for automated data processing, transformation and analytical calculations.
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Develop reusable functions and data services for automated business analysis.
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Support event-driven monitoring, anomaly detection and automated reporting workflows.
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Make structured datasets accessible to AI tools and LLMs through secure APIs and controlled data interfaces.
4. Engineering & Collaboration
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Collaborate with the Analytics Lead and existing Automation & AI team to develop scalable data solutions.
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Implement automated testing, pipeline monitoring and performance optimisation.
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Maintain clear technical documentation and version-controlled code.
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Independently troubleshoot data inconsistencies, integration failures and performance issues.
Required Skills:
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Python:
Strong proficiency in Pandas, scripting, API integrations and data processing.
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SQL:
Advanced querying, joins, aggregations, database design and optimisation.
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Databases:
Experience with PostgreSQL, MySQL, SQL Server or equivalent.
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Data Engineering:
Hands-on experience developing ETL/ELT pipelines and integrating multiple data sources.
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APIs & Automation:
Experience with REST APIs, authentication, data synchronisation and scheduled workflows.
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Engineering Practices:
Git, debugging, automated testing and production deployment.
Good to Have:
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Experience with Azure, Microsoft Fabric, dbt, Airflow, Dagster or equivalent tools.
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Familiarity with LLM APIs and AI-powered analytical applications.
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Experience working with e-commerce, FMCG, retail or ERP datasets.
Ideal Candidate:
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A
strong hands-on engineer
who enjoys building reliable data systems.
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Has independently designed, developed and deployed production data pipelines.
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Comfortable handling large, fragmented and inconsistent datasets.
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Strong problem-solving ability with attention to data accuracy and reliability.
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Resourceful, execution-oriented and comfortable working in a lean, fast-moving team.
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Interested in leveraging AI and modern development tools to accelerate engineering workflows.
What Success Looks Like:
Within the first 90 days, you should have:
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Integrated priority business data sources into a centralised analytical platform.
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Built reliable, automated data pipelines with appropriate monitoring and error handling.
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Developed clean, standardised and reusable datasets supporting business analytics.
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Enabled the Analytics Lead to build automated, AI-powered insights on top of trustworthy data.
Why Join MARS?
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Opportunity to build the data infrastructure for a rapidly growing Indian cosmetics brand.
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Work on real-world engineering challenges across e-commerce, sales, supply chain and finance.
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Significant technical ownership and freedom to adopt modern tools and technologies.
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Be part of a lean team building AI-native business intelligence capabilities.
