Project Description :

Design, develop, and maintain AWS-based data integration solutions that transform and connect data from relational databases into enterprise Knowledge Graphs using Amazon Neptune. The role requires strong expertise in SQL, Python, and semantic data modeling to deliver scalable, high-quality data pipelines and graph-based data solutions that enable knowledge discovery, metadata management, and cross-domain data integration.

Tech Stack :

- Amazon Neptune (RDF Knowledge Graphs)

- AWS Data Services (S3, Glue, Lambda, Step Functions, Athena, RDS)

- SQL Development & Data Modeling

- Python for ETL, Data Transformation, and Automation

- RDF, SPARQL, and Semantic Data Modeling

- Relational-to-Graph Data Migration

- Data Integration & Metadata Management

- Data Quality, Validation, and Governance

- Graph Query Optimization & Performance Tuning

- CI/CD and Infrastructure as Code (Terraform)

Ideal Profile :

Data Engineer with strong SQL and Python skills, hands-on AWS experience, and practical expertise in Amazon Neptune, RDF, and SPARQL. Proven ability to deliver data migration and integration solutions that bridge traditional relational systems with enterprise Knowledge Graph platforms.

Responsibilities :

- Design and build data pipelines to ingest and transform data from relational databases into Amazon Neptune.

- Develop RDF models and semantic representations of enterprise data.

- Create and optimize SQL and SPARQL queries for analytics and data retrieval.

- Develop Python-based frameworks for data transformation, validation, and automation.

- Support ontology-driven and metadata-centric data integration initiatives.

- Ensure scalability, reliability, and operational excellence of AWS data platforms.