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Mid-level role at reputable multinational increases competition, though niche knowledge-graph and AI-SDLC skills narrow applicant pool.
High because specialized data engineering, knowledge-graph, and supply-chain domain skills limit cross-industry portability.
Mandatory 3+ years and many specific cloud, graph, and AI-SDLC requirements enforce strict screening.
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Lead design and maintenance of data pipelines, knowledge graphs, and AI-ready platforms fueling supply chain AI/ML models within GCP environment.
Implement AI-native data engineering SDLC including agentic workflows and AI-assisted coding to deliver scalable, high-quality data for decision intelligence and generative AI applications.
Develop data validation, versioning, observability frameworks, reusable data contracts and standards to scale AI-centric data infrastructure across business units.
Bachelor’s degree in Computer Science, Data Science, or related technical field.
3+ years of experience in AI/ML, Data Engineering or Data Science with production-grade delivery in large enterprises.
Proficiency in Python, SQL, distributed data processing frameworks (Spark, Beam, Dataflow), graph query languages (Cypher, Gremlin), and data pipeline orchestration tools (Airflow, Dagster, Cloud Composer).
Strong knowledge of data modeling, warehousing, cloud services (GCP/BigQuery/Dataflow/Cloud Storage), and implementation of AI-specific SDLCs.
Experience bridging data engineering with supply chain domain expertise, including demand planning, logistics, risk management, and resilience.
Skilled in integrating and standardizing complex data sources into graph-based ontologies to support decision intelligence and generative AI scenarios.
Proven ability to lead AI-enhanced software development lifecycles and adopt novel AI tools to accelerate data engineering deliverables.