Senior Java Developer: Spring Boot & Data Pipelines

EXL; New York (NY); On-site; USD 130,000 - 150,000; Full time

Job summary

EXL is seeking a Data Engineer in New York to design and build scalable data solutions using Java and Spring Boot. You will develop backend services, data pipelines, and API-driven integrations for enterprise systems.

You will work in a distributed, fast-paced team, deliver end-to-end data engineering initiatives from development to deployment, and help optimize high-volume processing with Docker and Kafka.

Qualifications

• Strong Java development experience.
• Spring Boot experience.
• Experience with Apache Kafka for event-driven architectures.
• Strong Oracle SQL experience.
• Experience building and consuming REST APIs.
• Familiarity with JSON/XML data formats.
• Docker/containerization knowledge.
• MongoDB or other NoSQL experience is a plus.

Responsibilities

• Design, build, and unit test Java-based applications for data processing
• Develop backend services using Spring Boot and REST APIs
• Build data pipelines integrating data from Relational databases (Oracle)
• Implement event-driven data pipelines using Kafka (or MSK)
• Develop and expose APIs for data access and integration
• Work with structured and semi-structured data formats (JSON, XML, etc.)
• Optimize application performance for high-volume data processing
• Deploy applications using Docker containers
• Participate in code deployment and release processes
• Monitor and troubleshoot applications
• Create documentation for usage, deployment, and support
• Participate in agile development (scrum) and collaborate with cross-functional teams

Skills

Java

Spring Boot

Kafka

Oracle SQL

REST APIs

Data processing patterns

JSON/XML

Docker

MongoDB

Job description

EXL is seeking a Data Engineer in New York to design and build scalable data solutions using Java and Spring Boot. You will develop backend services, data pipelines, and API-driven integrations for enterprise systems.

You will work in a distributed, fast-paced team, deliver end-to-end data engineering initiatives from development to deployment, and help optimize high-volume processing with Docker and Kafka.