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Mid-level metro-based Data Engineer with common title and 5+ years attracts many qualified applicants.
Specialized GenAI data engineering for ITSM and enterprise systems reduces cross-industry transferability.
Explicit 5+ years, 2+ years GenAI plus specific vector, RAG, and platform experience required.
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Build and maintain secure, AI-ready data pipelines and retrieval architectures integrating enterprise operational data from systems like ServiceNow, SharePoint, and Git.
Design and optimize semantic search, vector storage, metadata tagging, and knowledge mapping to enhance AI agent performance in grounded retrieval tasks.
Ensure strict data quality, lineage, governance, access control, privacy, and compliance for AI data retrieval and integration.
5+ years of data engineering or platform development experience, with 2+ years supporting GenAI, RAG, semantic search, or related AI data products (ITSM/ServiceNow experience preferred).
Strong expertise in AI agent data integration, embeddings, vector databases (e.g., Azure AI Search, Pinecone, Elasticsearch), and RAG pipeline configuration.
Proficiency in Python, SQL, ETL/ELT, data modeling, and orchestration tools like Databricks, Snowflake, BigQuery, Airflow, or dbt.
Deep understanding of enterprise data governance, lineage, access controls, auditability, and sensitive data handling for AI applications.
Experienced in operationalizing AI retrieval architectures for enterprise IT data sources and ITSM platforms.
Skilled in developing scalable, governed data solutions enabling high-accuracy vector search and AI agent context provisioning.
Capable of delivering end-to-end AI-ready pipelines, metadata models, and retrieval performance evaluations in compliance-driven environments.