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Known global brand, mid-level generalist role, metro location, broad skills increase candidate competition.
Core data engineering and MLOps skills are transferable, ERP and supply-chain knowledge increases domain specificity.
Explicit 4–6 years and many mandatory data, MLOps, and cloud tool requirements.
Design, develop, and maintain scalable ETL/ELT pipelines ingesting data from SAP S/4HANA, Oracle, Salesforce, and third-party APIs into data lakes/lakehouses.
Build and optimize medallion-architecture data products on platforms such as Databricks incorporating data quality, lineage, and governance controls using tools like Purview, Alation, or Unity Catalog.
Develop, deploy, and operationalize ML and GenAI solutions (including RAG pipelines and vector databases) with MLOps practices across cloud services (Azure, AWS, GCP).
4–6 years of work experience in data engineering, ML engineering, or applied AI roles.
Proven track record delivering at least 2–3 production-grade data or AI projects end-to-end.
Experience with enterprise ERP data sources like SAP S/4HANA and Oracle, and integration patterns (CDC, IDoc, OData, APIs).
Work Experience Required: 4–6 years explicitly mentioned
Experienced in end-to-end implementation of data and AI production-grade projects within enterprise environments.
Strong expertise in cloud-native data engineering and ML tooling (Databricks, MLOps frameworks, Azure ML, SageMaker, Vertex AI).
Familiarity with integrating data from ERP systems and applying analytics/use cases related to supply chain, procurement, or logistics.