Lead – Software Engineering (Global Data Network Platform)

About the Company

Join a globally recognized digital transformation and technology services organization that partners with Fortune 500 enterprises across industries including financial services, healthcare, retail, manufacturing, and telecommunications. The company specializes in building enterprise-scale cloud platforms, AI-driven solutions, cybersecurity products, and digital engineering services. This opportunity is part of a high-impact engineering team focused on developing next-generation fraud intelligence and data-sharing platforms using modern cloud-native technologies.

Job Summary

We are looking for an experienced Lead II - Software Engineering with strong backend engineering expertise to build and modernize a next-generation Global Data Network (GDN) platform.

The platform enables secure, scalable, and real-time sharing of fraud intelligence across multiple enterprise systems. It manages complex fraud-related entities such as IP addresses, devices, cookies, merchants, beneficiary accounts, and other connected identities using graph-based data models.

The ideal candidate should possess strong backend engineering skills, cloud-native application development experience on Microsoft Azure, and the ability to design scalable distributed systems. Exposure to Graph Databases and full-stack development is highly preferred.

This role offers an opportunity to work on cutting-edge fraud analytics platforms involving Graph Technologies, Cloud Engineering, APIs, and Large-scale Data Processing.

Key Responsibilities

Platform Engineering

1. Design, develop, and modernize the Global Data Network platform.
2. Transform legacy database and CSV-based distribution into cloud-native API-driven architecture.
3. Build scalable backend services for fraud intelligence sharing.
4. Develop highly available and secure enterprise applications.

Backend Development

1. Design and implement scalable backend microservices.
2. Develop RESTful APIs for real-time data exchange.
3. Build reusable, maintainable, and secure application components.
4. Optimize application performance and scalability.

Graph Data Engineering

1. Design graph-based data models.
2. Model relationships between fraud entities.
3. Build graph traversal and relationship analysis capabilities.
4. Enable multi-hop fraud detection and entity correlation.

Azure Cloud Development

1. Build cloud-native applications on Microsoft Azure.
2. Develop scalable cloud services.
3. Implement cloud deployment strategies.
4. Optimize application reliability and performance.

Database Design

1. Design highly scalable database solutions.
2. Evaluate Graph DB vs Relational DB approaches.
3. Optimize data storage and retrieval.
4. Build high-performance data processing pipelines.

API Development

1. Replace batch-based integrations with API-driven communication.
2. Develop secure REST APIs.
3. Support system integrations.
4. Enable near real-time data synchronization.

Full Stack Development

1. Develop backend-heavy applications.
2. Contribute to UI development where required.
3. Build visualization interfaces for graph relationships.
4. Support interactive graph exploration features.

Architecture Contribution

1. Participate in architecture discussions.
2. Recommend appropriate cloud and database technologies.
3. Design scalable enterprise solutions.
4. Drive engineering best practices.

Required Qualifications

1. 7–9 years of software engineering experience.
2. Strong expertise in Java (preferred) or Python.
3. Excellent backend development experience.
4. Strong understanding of distributed systems.
5. Hands-on Microsoft Azure experience.
6. Experience building REST APIs.
7. Strong database design knowledge.
8. Experience with scalable cloud applications.
9. Understanding of microservices architecture.
10. Experience designing enterprise-grade software.

Preferred Qualifications

1. Experience with Graph Databases (Neo4j, Cosmos DB Gremlin, JanusGraph, TigerGraph, Amazon Neptune, etc.).
2. Fraud detection platform experience.
3. Knowledge graph implementation.
4. Event-driven architecture.
5. Containerization (Docker/Kubernetes).
6. CI/CD implementation.
7. Azure DevOps.
8. Basic frontend development.
9. Graph visualization frameworks.
10. Real-time data processing.
11. Agile/Scrum methodology.

Technical Skills

Backend

1. Java (Preferred)
2. Python
3. Spring Boot
4. REST APIs
5. Microservices

Cloud

1. Microsoft Azure
2. Azure App Services
3. Azure Functions
4. Azure Storage
5. Azure DevOps

Database

1. Graph Databases
2. Neo4j
3. Azure Cosmos DB
4. SQL
5. Database Design

Architecture

1. Distributed Systems
2. Cloud Native Applications
3. API Design
4. Scalable Systems
5. Event-driven Architecture

Frontend (Basic)

1. JavaScript
2. HTML
3. CSS
4. Graph Visualization Libraries