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Tier-1 brand and mid-level experience range increase applicant density despite niche AI-data requirements.
Low — core data engineering skills are broadly transferable across industries despite AI-specific preferences.
Medium: explicit 0–7 years plus required Python/SQL and ETL knowledge; many advanced skills preferred.
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Build and maintain scalable backend data pipelines and ETL/ELT workflows for AI applications.
Contribute to development of retrieval-augmented generation (RAG) pipelines, vector indexing, and knowledge graph assets to support enterprise AI use cases.
Perform data quality checks, validation, and optimization of batch and near-real-time data workflows.
Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or related field.
0–7 years of experience in data engineering, software engineering, or related technical role.
Working knowledge of Python and SQL and/or Java.
Basic understanding of ETL/ELT pipelines, data integration, and data transformation concepts.
Experience or exposure to big data/distributed processing tools like Spark or Databricks, indicating ability to work with enterprise-scale data platforms.
Familiarity with knowledge graph concepts, semantic search, vector databases, or RAG pipelines demonstrating alignment with AI-powered data systems.
Comfortable contributing under guidance in a data engineering role supporting AI use cases in an enterprise environment.