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Tier‑1 brand and metro location but senior, specialized data role gives moderate applicant competition.
Role requires deep enterprise systems and knowledge-graph expertise, reducing cross-industry portability.
Extensive mandatory enterprise data, knowledge-graph and vector database skills required, creating strict technical filters.
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Own and architect the end-to-end data pipeline powering an agentic AI platform, including ingestion, transformation, enrichment, and semantic modelling.
Design and build production-grade ETL/ELT pipelines integrating structured (ERP, CRM, OSS, BSS, billing, finance, HR) and unstructured data (emails, documents, logs, transcripts) at enterprise scale.
Implement and maintain knowledge graph and ontology pipelines, data quality automation frameworks, and integrations with vector databases for AI-ready data access.
Strong hands-on experience with Python, SQL, PySpark, Apache Spark, and modern data pipeline development.
Proven experience in enterprise data engineering including schema discovery, data profiling, ontology implementation, and knowledge graph integration.
Work experience with enterprise systems such as ERP, CRM, OSS, BSS, billing, finance, ServiceNow, Salesforce, SAP, Oracle, or legacy databases.
Work Experience Required: Not explicitly mentioned in the JD.
Senior individual contributor comfortable owning end-to-end data architecture and pipeline design in complex enterprise environments.
Experienced with both structured and unstructured data ingestion and transformation at enterprise scale, and familiar with AI data applications including semantic modelling and vector databases.
Background in telecom, BFSI, manufacturing, or similar complex domains with exposure to OSS/BSS, ERP, CRM, billing, or network inventory systems.