Role Overview: We are seeking a versatile Data Engineer with a hybrid background in traditional data engineering (SQL), semantic modeling (Ontologies), Graph Databases, and Machine Learning systems. In this role, you will design, build, and optimize the connected data architecture that powers our Enterprise Knowledge Graph (EKG), feature stores, and advanced ML pipelines.
You will bridge relational systems, graph databases, and ML models—building scalable data pipelines (ETL/ELT), transforming raw data into structured ontologies, and preparing graph-native features for downstream AI/ML applications like GraphRAG, link prediction, and recommendation systems.
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Key Responsibilities

Data Engineering & Pipeline Architecture• Scalable Data Pipelines: Design, implement, and maintain high-throughput batch and streaming ETL/ELT data pipelines using SQL, Python, and contemporary data orchestration frameworks (e.g., Airflow, Prefect, dbt).
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• Relational & Analytical Data Stores: Write complex SQL queries, procedures, and transformations across data warehouses (e.g.,



Snowflake, BigQuery, PostgreSQL) to clean, aggregate, and stage raw business data.
• Data Integration: Extract structured, semi-structured, and unstructured data from transactional databases, APIs, and file systems to map into graph-ready schemas.
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Knowledge Graph & Ontology Engineering• Graph Modeling: Build and optimize graph data schemas and property graph models (nodes, edges, labels) aligned with business domain ontologies.
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• Semantic Mapping & Ingestion: Translate semantic concepts (OWL, RDF, SKOS, SHACL) and relational SQL schemas into Graph Database models (Property Graphs or Triple Stores).
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• Graph Database Management: Administer, query, and tune performance on graph databases (e.g., Neo4j, AWS Neptune, Stardog, GraphDB) using languages like Cypher, Gremlin, or SPARQL.
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Machine Learning & AI Integration• Grap

📌 Data Engineer (Chennai)
🏢 Giggso
📍 Chennai