Senior Software Engineer

Responsibilities:

• Design and develop scalable, high-performance microservices for international eCommerce returns workflows using Java/J2EE and Spring Boot.
• Architect and implement event-driven processing pipelines using Kafka for asynchronous, decoupled communication between services.
• Build and maintain RESTful and gRPC APIs following best practices for reliability, versioning, and backward compatibility.
• Design and optimise data models across relational (SQL) and non-relational (NoSQL) databases to support complex returns workflows.
• Apply design patterns (SOLID, DDD, CQRS, Saga) to ensure code is maintainable, extensible, and production-ready.
• Leverage AI-powered development tools (e. g., GitHub Copilot, Wibey, ChatGPT) to accelerate code generation, debugging, code reviews, test writing, and documentation.
• Collaborate with architects and senior engineers on system design, technical roadmaps, and capacity planning.
• Ensure high availability and fault tolerance through proactive monitoring, alerting, and incident response.
• Integrate with upstream and downstream systems (order management, logistics, payments) via messaging and APIs.
• Participate in agile ceremonies: sprint planning, grooming, and retrospectives, contributing to team velocity and quality.

Requirements:

• 6 to 10 years of total experience with a strong Java backend focus; minimum 5+ years in backend engineering platform development.
• 5+ years of experience in Java technologies (Java 17+), Spring Boot, distributed systems, and large-scale application development and design.
• Hands-on experience with Apache Kafka, SQL/NoSQL databases (Azure SQL, Cosmos DB, or equivalent).
• Experience with microservices architecture, service decomposition, API contracts, circuit breakers, and service discovery.
• Proficiency in applying Gang-of-Four, enterprise integration, and domain-driven design patterns; experience with event-driven design.
• Ability to lead and contribute to system design discussions, capacity planning, trade-off analysis, scalability, and fault tolerance.
• Proficiency in using AI coding assistants (GitHub Copilot, ChatGPT, Claude, or equivalent) for code generation, refactoring, test scaffolding, PR reviews, and documentation with demonstrated ability to critically evaluate AI-generated output for correctness, security, and performance.
• Understanding of prompt engineering patterns (chain-of-thought, few-shot, system prompts) to get reliable, high-quality outputs from LLMs.
• Experience with containerization technology (Kubernetes/WCNP) and CI/CD pipelines.