Senior Java Backend Service Developer
Budget: $750 – $1,500 USD
We are looking for a Senior Java Backend Developer with deep expertise in high-performance data architectures to join our team. As we scale our career pathway platform, your primary focus will be optimizing how we store, retrieve, and search complex datasets. You will be responsible for bridging our robust Java/Spring services with a modern data stack centered around MongoDB and Elasticsearch to provide our users with lightning-fast, relevant results.
Key Responsibilities
* Data Architecture: Design and implement scalable data models in MongoDB, ensuring efficient document structures for high-speed read/write operations.
* Search Optimization: Architect and maintain Elasticsearch clusters, including index management, custom mapping, and complex query DSL optimization to power advanced search features.
* Backend Engineering: Develop and scale microservices using Java 17+ and Spring Boot, integrating seamlessly with NoSQL and Search engines.
* Data Synchronization: Build and monitor reliable data pipelines (using tools like Kafka, Change Data Capture, or Logstash) to ensure real-time consistency between MongoDB and Elasticsearch.
* Performance Tuning: Identify and resolve bottlenecks in database performance, query latency, and indexing overhead.
* API Excellence: Create high-throughput RESTful APIs that leverage the full power of full-text search, filtering, and aggregations.
* Mentorship: Lead technical discussions on NoSQL best practices and mentor the team on evolving our search capabilities.
Required Skills & Qualifications
* Java Expertise: 6+ years of professional experience in Java development with a strong command of the Spring Ecosystem (Spring Boot, Data, Security).
* NoSQL Mastery: Extensive experience with MongoDB, including aggregation frameworks, indexing strategies, and sharding/replication.
* Search Engine Proficiency: Proven track record of implementing Elasticsearch at scale, including tuning relevance scoring and managing large-scale clusters.
* Integration: Solid experience with Spring Data MongoDB and Spring Data Elasticsearch.
* Infrastructure: Hands-on experience with Docker, Kubernetes, and cloud providers (AWS/GCP/Azure).
* Testing: Commitment to high code quality through JUnit, Mockito, and integration testing for data layers.
Preferred Qualifications
* Experience with Elastic Stack (ELK) for observability and logging.
* Familiarity with event-driven architectures to handle asynchronous data updates.
* Knowledge of AI-driven search enhancements (e.g., vector search or k-NN) within Elasticsearch.
Key Responsibilities
* Data Architecture: Design and implement scalable data models in MongoDB, ensuring efficient document structures for high-speed read/write operations.
* Search Optimization: Architect and maintain Elasticsearch clusters, including index management, custom mapping, and complex query DSL optimization to power advanced search features.
* Backend Engineering: Develop and scale microservices using Java 17+ and Spring Boot, integrating seamlessly with NoSQL and Search engines.
* Data Synchronization: Build and monitor reliable data pipelines (using tools like Kafka, Change Data Capture, or Logstash) to ensure real-time consistency between MongoDB and Elasticsearch.
* Performance Tuning: Identify and resolve bottlenecks in database performance, query latency, and indexing overhead.
* API Excellence: Create high-throughput RESTful APIs that leverage the full power of full-text search, filtering, and aggregations.
* Mentorship: Lead technical discussions on NoSQL best practices and mentor the team on evolving our search capabilities.
Required Skills & Qualifications
* Java Expertise: 6+ years of professional experience in Java development with a strong command of the Spring Ecosystem (Spring Boot, Data, Security).
* NoSQL Mastery: Extensive experience with MongoDB, including aggregation frameworks, indexing strategies, and sharding/replication.
* Search Engine Proficiency: Proven track record of implementing Elasticsearch at scale, including tuning relevance scoring and managing large-scale clusters.
* Integration: Solid experience with Spring Data MongoDB and Spring Data Elasticsearch.
* Infrastructure: Hands-on experience with Docker, Kubernetes, and cloud providers (AWS/GCP/Azure).
* Testing: Commitment to high code quality through JUnit, Mockito, and integration testing for data layers.
Preferred Qualifications
* Experience with Elastic Stack (ELK) for observability and logging.
* Familiarity with event-driven architectures to handle asynchronous data updates.
* Knowledge of AI-driven search enhancements (e.g., vector search or k-NN) within Elasticsearch.