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Mid-level data engineer title and 5+ years increase applicants, but niche graph/Neo4j skills moderate competition.
Graph data engineering skills are transferable, though BFSI domain preference increases specificity.
Explicit 5+ years and mandatory Neo4j/Python/graph modeling impose strict filtering.
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Design and implement BFSI ontology models, graph schemas, entity and relationship extraction pipelines.
Develop and optimize graph ingestion, transformation logic, traversal, and query patterns for BFSI engineering use cases.
Collaborate with AI engineers to support GraphRAG implementation and improve graph performance and semantic accuracy.
5+ years experience in designing and implementing ontology models and graph data pipelines, preferably in BFSI domain.
Proficiency in Neo4j and Cypher for graph database usage.
Strong skills in Python data engineering and SQL for data transformation.
Experience with entity relationship modeling and metadata/lineage concepts.
Experienced in BFSI domain data engineering with knowledge of graph structures and semantic enrichment.
Comfortable working on graph traversal, query optimization, and performance tuning.
Able to collaborate with AI teams to integrate graph data solutions for retrieval applications.