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Tier-1 brand, metro location, and broad senior specialized skillset drive high competition.
Role requires niche knowledge-graph and RAG expertise but core data engineering skills remain reasonably transferable across industries.
Explicit 8–14 years plus mandatory deep data, knowledge-graph, and platform engineering skills indicates high strictness.
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Lead design and evolution of enterprise AI data and knowledge platforms including ingestion, transformation, storage, and retrieval layers.
Define and enforce standards for scalable batch/streaming data pipelines, ETL/ELT frameworks, data contracts, lineage, governance, and quality controls.
Coach and develop team members; drive adoption of modern data engineering practices and represent Data/Knowledge function in cross-functional architecture and governance forums.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.
8–14 years of experience in data engineering, data architecture, platform engineering, or large-scale data systems.
Deep expertise in scalable data pipelines, ETL/ELT frameworks, big data processing technologies (e.g., Spark, Databricks, Flink), and AI data architectures (knowledge graphs, RAG, ontologies).
Work Location: On-premise; Cloud certifications (AWS/Azure) are a plus but not mandatory.
Experienced leader able to influence architecture decisions across multiple domains and lead technical teams.
Strong background in designing and operating enterprise-grade AI-ready data platforms with end-to-end ownership of data governance and metadata management.
Comfortable working on complex, enterprise-scale systems integrating big data technologies and AI knowledge frameworks in a secure, governed environment.