Senior Java Software Engineer

Requirements – JAVA, spring Boot, Restful, DS Algo, Experience in building, optimizing, and maintaining large data pipelines , building distributed data services and solutions.

Development, Testing, and deployment experience with GitHub, Docker / Kubernetes

Job Description :

Strong expertise in Java and the Spring Framework, particularly Spring Boot.

Good understanding of SQL oracle, SQL server or similar languages.

Core Engineering : Data structures & algorithms, testing (Junit etc), Git, clean code.

Experience with graph databases (e.g., Neo4j, JanusGraph) and graph query languages.

Ops & CI / CD : Monitoring (Prometheus / Grafana), logging, pipelines (Jenkins / GitHub Actions).

Cloud Native : Docker, Kubernetes (deploy, network, scale, troubleshoot).

6+ years of directly applicable experience

BS in Computer Science, Engineering, or equivalent experience.

About the team –

Our Cloud Data Technologies team oversees data infrastructure and the management of the end-to-end data lifecycle.

Project and role –

As a Software Engineer on our team, you will work on our Hadoop-based data warehouse and cloud development environment, contributing to scalable and reliable big data solutions and services for analytics and business insights. This is a hands-on role focused on building, optimizing, and maintaining large data pipelines , building distributed data services and solutions.

Key Responsibilities

Design, develop, and maintain robust data solutions and services and related Hadoop ecosystems, ensuring data reliability, scalability, and performance.

Optimize and troubleshoot distributed systems for ingestion, storage, and processing.

Collaborate with data engineers, analysts, and platform engineers to align solutions with business needs.

Ensure data security, integrity, and compliance throughout the infrastructure.

Maintain documentation and contribute to architecture reviews.

Participate in incident response and operational excellence initiatives for the data warehouse.

Continuously learn and apply new Hadoop ecosystem tools and data technologies.