Data Engineer – Enterprise Data & AI
Marlabs Bengaluru, Karnataka, India
Business Consulting and Services · 1,001-5,000 employees
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About the role
Design, build, and maintain scalable data pipelines and retrieval layers to support enterprise AI agents and analytics. Collaborate with cross-functional teams to integrate structured and unstructured data across various enterprise platforms while ensuring data quality and governance.
What they look for
Requirements
Requires 5+ years of experience in data engineering with strong proficiency in Python and SQL. Candidates must have hands-on experience with API integrations and data modeling within complex enterprise environments.
Full description
Marlabs, a global AI and Digital Solutions Consulting firm, delivers intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest healthcare, life sciences, financial services, and government organizations worldwide. As we continue to expand our global footprint, we have an exciting opportunity for a highly skilled Data Engineer – Enterprise Data & AI to join our innovative and dynamic team.
Data Engineer – Enterprise Data & AI | About You
As a Data Engineer – Enterprise Data & AI, you will build and operate the governed data foundation that powers enterprise AI agents, analytics, and intelligent business applications. You will develop production-grade data pipelines, connectors, and retrieval layers that integrate structured and unstructured information across enterprise platforms. The ideal candidate combines strong Python, SQL, API integration, data modeling, and data governance expertise with experience integrating complex systems such as ERP, CRM, collaboration platforms, and modern enterprise data platforms.
Data Engineer – Enterprise Data & AI | Day-to-Day
- Design, build, and maintain scalable data pipelines, connectors, and retrieval layers that enable secure access to structured and unstructured enterprise data for AI and analytics use cases.
- Integrate data across enterprise platforms including ERP systems, Salesforce, SharePoint, Teams, Outlook, Palantir Foundry, and other internal and external data sources.
- Develop and maintain data models supporting commercial and operational information such as RFQs, quotes, product specifications, BOMs, part numbers, pricing, costs, margins, contracts, customer history, complaints, and opportunities.
- Build data ingestion, transformation, and retrieval workflows using Python, SQL, REST APIs, Spark or Polars, ensuring scalability, reliability, and production performance.
- Implement data quality, metadata, lineage, security, access controls, and refresh processes to ensure enterprise information remains trusted, traceable, and appropriately governed.
- Develop governed retrieval capabilities for AI agents and enterprise applications, including processing of unstructured documents and combining information across multiple data sources.
- Collaborate with AI, application, data architecture, and business teams to design production-ready data solutions, including secure and controlled system write-back where required.
Data Engineer – Enterprise Data & AI | Skills & Experience
- 5+ years of Data Engineering experience building and supporting production data platforms, data pipelines, integrations, and enterprise data solutions.
- Strong hands-on expertise in Python and SQL, with experience building data ingestion, transformation, processing, and automation workflows; experience with Spark or Polars is highly valued.
- Strong experience with APIs and enterprise system integrations, including connecting data from ERP, CRM, SaaS, collaboration, and operational systems.
- Solid understanding of data modeling, data quality, metadata, lineage, security, and access-control principles for production enterprise data platforms.
- Experience supporting structured and unstructured data retrieval for AI/ML or intelligent applications, including document processing and governed enterprise information access.
- Experience with Palantir Foundry, Salesforce, ERP platforms, Microsoft 365 sources, product/BOM master data, commercial data, or controlled CRM write-back is highly desirable.
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