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Mid-level 4–6 years, metro location, and broad cross-cloud/data+ML skillset increase applicant competition.
Core data and ML skills transfer well, but ERP and supply-chain domain needs raise industry specificity.
Explicit 4–6 years plus mandatory 2–3 production projects and extensive tech requirements increase screening rigor.
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Design, build, and maintain scalable ETL/ELT pipelines ingesting structured and unstructured data from SAP S/4HANA, Oracle, Salesforce, and third-party APIs into enterprise data lakes/lakehouses.
Develop and optimize data products using medallion architecture on Databricks and implement data quality, lineage, and governance controls with tools like Purview, Alation, or Unity Catalog.
Build, deploy, and monitor machine learning models including GenAI and LLM-powered solutions; operationalize models through MLOps practices and deploy data/AI workloads on cloud platforms (Azure, AWS, GCP).
4–6 years of professional experience in data engineering, ML engineering, or applied AI roles.
Proven delivery of at least 2–3 production-grade data or AI projects end-to-end.
Experience working with enterprise ERP data (SAP S/4HANA, Oracle) and integration patterns (CDC, IDoc, OData, APIs) is preferred but not mandatory.
Work Experience Required: 4–6 years; Notice Period: Not explicitly mentioned in the JD.
Experienced in designing end-to-end data pipelines and productizing ML/AI models with strong cloud deployment skills, especially on Azure, AWS, or GCP.
Familiarity with enterprise ERP systems and integration patterns suggests the role suits candidates with supply chain, procurement, or logistics analytics exposure.
Comfortable working in a global team ecosystem and implementing MLOps, GenAI, LLM solutions, and advanced data governance frameworks.