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Strong employer brand, generalist mid-level data-engineer role with 5–12 years increases applicant competition.
Core data engineering skills are broadly transferable across industries despite some AI/knowledge graph specifics.
Explicit 5–12 years requirement plus required Python/SQL and data engineering skills create moderate screening strictness.
Build and maintain scalable backend data pipelines and ETL/ELT workflows for AI applications.
Develop and support data integration, retrieval pipelines, and structured knowledge assets such as knowledge graphs and vector indexing workflows.
Perform data quality checks, optimization, and documentation to ensure reliable GenAI use cases.
Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or related field.
5–12 years of experience in data engineering, software engineering, or related technical roles.
Working knowledge of Python and SQL and/or Java.
Basic understanding of ETL/ELT pipelines, data transformation, and data integration concepts.
Experienced in big data or distributed processing tools such as Spark or Databricks.
Familiar with data modeling, metadata tagging, semantic enrichment, and knowledge graph foundations.
Comfortable working under guidance from senior engineers and participating in collaborative engineering workflows.