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Tier-1 brand plus mid-level role increases competition, but niche knowledge-graph and AI-data requirements moderate applicant density.
Core data-engineering skills are transferable across industries, but supply-chain and knowledge-graph expertise raise domain sensitivity to medium.
Explicit 3+ years plus mandatory GCP, data pipeline, graph and AI-SDLC skills create strict shortlisting filters.
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Engineer and maintain AI-ready data pipelines, knowledge graphs, and data platforms to fuel supply chain AI/ML models and Enterprise Knowledge Graphs.
Implement and standardize AI-native data engineering SDLC including agentic workflows, AI-assisted coding, automated testing, and data quality frameworks.
Lead technical design and delivery of scalable, reliable data ingestion, transformation, and storage pipelines in GCP to support decision intelligence and Generative AI applications for global supply chain.
Bachelor’s degree in Computer Science, Data Science, or related technical field.
3+ years of experience in AI/ML, Data Engineering, or Data Science delivering production-grade solutions in large enterprises.
Proficiency in Python, SQL, distributed data processing frameworks (Spark, Beam, Dataflow), Graph Query Languages (Cypher, Gremlin).
Experience with data pipeline orchestration tools (Airflow, Dagster, Cloud Composer), CI/CD for data pipelines, and AI-specific SDLC implementation.
Technical lead with blend of hands-on expertise in data engineering, AI/ML data enablement, and platform architecture specialized in supply chain domain.
Experience driving adoption of AI-assisted development practices and familiarity with agentic workflows and LLM tools to accelerate delivery with high quality.
Proficient in designing graph-based data models and integrating enterprise data sources for advanced decision intelligence and Generative AI use cases.