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We are looking for a high-performing Java Engineer to join our team managing large-scale data pipelines processing billions of records. You will be responsible for building and optimizing high-throughput services that interface with Kafka, Solr, and HDFS within a Kubernetes environment. The ideal candidate has a "under the hood" understanding of how Java interacts with Linux and distributed storage.
Job Responsibility
Design & Development: Build robust, scalable, and maintainable Java applications using Spring Boot or Microservices architecture
Data Orchestration: Develop and optimize Kafka producers/consumers, ensuring efficient data serialization and compression (Snappy/LZ4)
Big Data Integration: Manage data lifecycle operations within HDFS and perform high-speed indexing and querying in Solr
Performance Tuning: Diagnose and resolve bottlenecks in JVM memory management, Garbage Collection, and JNI-based native library interactions
Cloud Native Deployment: Containerize applications using Docker and manage deployments in Kubernetes (K8s) across Oracle Linux environments
Troubleshooting: Solve complex issues related to distributed locking, file system consistency, and network latency at scale.
Requirements
Core Java: Strong expertise in Java 8+ (Collections, Multithreading, Concurrency, and Stream API)
Messaging: Deep understanding of Apache Kafka (partitioning strategies, consumer groups, and offset management)
Storage/Search: Hands-on experience with HDFS and Solr (sharding, collection management, and query optimization)
DevOps: Proficiency with Docker and Kubernetes (writing Dockerfiles, managing volumes, and understanding K8s security contexts)
Linux: Solid command-line skills (Oracle Linux/RHEL preferred) and experience troubleshooting native library dependencies (glibc vs musl).