Role will operate primarily in feature engineering support for the Fraud AI/ML modeling team, with a focus on feature development and graph-based data assets. Personnel will engineer, maintain, and optimize features within the AWS Feature Store using PySpark; create and enhance graph relationships, node structures, and embeddings within Neo4j; support data quality monitoring and feature governance; and partner with modelers to enable scalable, production-ready data assets for fraud detection and risk analytics.

This role requires individuals with strong communication, problem-solving, and independent thinking skills.

Preferred Platform Experience:

- Experience working on AWS (or similar platform such as Azure / GCP)
- Experience developing data pipelines using PySpark
- Experience working with Neo4j and graph data modeling concepts
- Familiarity with feature stores, embeddings,



and entity resolution techniques
- Essential – Experience engineering and deploying production-grade data features or pipelines in an enterprise workplace
- Essential – Experience with graph analytics, knowledge graphs, embeddings, or network-based modeling techniques
- Nice to Have – Experience with Fraud Analytics, Strategy, Modeling, or Financial Crimes use cases

Languages and Tools:

- Advanced (Essential) – Python

• Advanced (Essential) – PySpark

- Advanced (Preferred) – Neo4j (Cypher, graph modeling, embeddings)
- Intermediate (Essential) – GitHub / Bitbucket
- Intermediate (Preferred) – SQL
- Intermediate (Preferred) – AWS Feature Store, EMR, Glue, or related cloud data services

📌 Data Engineer (Hyderabad)
🏢 IntraEdge
📍 Hyderabad