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Mid-level Bangalore location and generalist data engineer title increase applicant competition despite niche supply-chain specialization.
Strong SAP/supply-chain and knowledge-graph requirements reduce cross-industry transferability.
Explicit 5-8 years, supply-chain domain mastery and specific technical stack requirements make filters stringent.
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Own and architect data pipelines integrating supply chain data into Cognite Data Fusion (CDF) to power industrial GenAI and knowledge graphs.
Lead design and packaging of standardized, scalable extractors connecting external logistics platforms and internal ERP/MES systems.
Collaborate across product, value delivery, customers, and partners to optimize data models and ensure robust, deployable integrations supporting AI-driven supply chain workflows.
5-8 years of experience in data engineering or similar, with a focus on production-grade data pipelines.
Proven expertise integrating external REST APIs from logistics and market intelligence platforms (e.g., FourKites).
Strong knowledge of supply chain domain data models, including SAP TMS, WMS, MM, PP, PM modules or equivalents.
Proficiency with Python, SQL, and working knowledge of cloud platforms (AWS, GCP, or Azure).
Experienced in designing complex data schemas optimized for relational and graph database models, particularly for knowledge graphs.
Skilled in end-to-end delivery of scalable data pipelines with a DevOps mindset (Git, CI/CD, clean code).
Domain expert in supply chain systems and industrial data, capable of technical consultation and cross-functional collaboration to align data integration with AI workflows.