Job Description:
Overview
Financial systems demand reliability, scale, and precision. This role centers on building and optimizing high‑performance backend services and cloud‑based data processing pipelines that support critical financial operations. You’ll work across Java/Spring microservices, AWS EMR/Spark workloads, and a mature testing culture (TDD/BDD) to deliver secure, resilient, and compliant software.
Key Responsibilities
- Design and develop enterprise‑grade backend services using Java 21, Spring Boot, and microservice patterns.
- Build and optimize distributed data processing pipelines leveraging AWS EMR, Apache Spark, and related cloud-native services.
- Implement automated testing frameworks using TDD, BDD, JUnit, Cucumber, and mocking libraries.
- Collaborate with cross-functional teams (cloud engineering, data engineering, architecture, QA, and product) to deliver secure, compliant financial systems.
- Enhance system reliability through observability, logging, metrics, and performance tuning.
- Participate in code reviews, architectural discussions, and continuous improvement initiatives.
- Ensure adherence to financial‑industry standards for security, auditability, and operational excellence.
Required Qualifications
- 5–7+ years professional experience as a Software Engineer or Backend Engineer.
- Strong proficiency in Java 21, Spring Boot, RESTful APIs, and microservices.
- Hands-on experience with AWS, including EMR, Spark, S3, Lambda, CloudWatch, or related services.
- Solid understanding of distributed systems, parallel processing, and data pipeline design.
- Demonstrated experience with TDD, BDD, Cucumber, and automated testing frameworks.
- Experience working in regulated environments or large-scale enterprise systems.
- Familiarity with CI/CD pipelines, Git, Jenkins, GitLab, or similar tooling.
Preferred Qualifications
- Experience in financial services, fintech, risk systems, or high‑throughput transaction platforms.
- Knowledge of Kafka, Kinesis, or other streaming technologies.
- Exposure to DevOps practices, infrastructure-as-code, or containerization (Docker, Kubernetes).
- Understanding of domain-driven design (DDD) and event-driven architectures.
- Performance tuning experience for low-latency, high-volume systems.