Back
Mid-level Pune role with niche knowledge-graph skills yields moderate applicant competition.
High because knowledge-graph databases and compliance/KYC domain expertise limit cross-industry transferability.
Explicit 3–5 year requirement plus mandatory graph DB and compliance/KYC expertise raises filter strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Ensure data quality, accuracy, and consistency by validating datasets across multiple graph and NoSQL databases including PostgreSQL, Neo4j, TigerGraph, NebulaGraph, ArangoDB, and MongoDB.
Verify benchmark query outputs for correctness and consistency, and trace root causes of data discrepancies including ETL issues and schema mismatches.
Produce data quality reports, maintain defect logs, and contribute to architecture assessments and technology recommendations as part of the Data Engineering team.
3–5 years of relevant work experience in data analysis or related field.
Hands-on experience with graph databases such as Neo4j, TigerGraph, or similar is mandatory.
Familiarity with graph data models (property graphs, node/edge schema, relationship taxonomies).
Knowledge in compliance, KYC/AML, financial services data domains including beneficial ownership and sanctions screening is required.
Experienced in working with large-scale entity relationship datasets like Bureau van Dijk/Orbis and knowledgeable in knowledge graph or ontology concepts.
Operates with strong analytical rigor evident by responsibilities relating to root cause analysis and statistical evaluation of data representativeness.
Comfortable in hybrid work environment based in Pune and collaborating closely with senior data engineers and architects for technology recommendations.