GridGain
GridGain is a Unified Real-Time Data Platform created by the original developers of Apache Ignite. It provides enterprises with a simplified and highly optimized data architecture that supports extreme speed, massive scale, and high availability for their data ecosystems. GridGain combines stream processing, a distributed in-memory data grid, and colocated compute to deliver data processing and analytics with ultra-low latencies. It is designed to seamlessly integrate both streaming data in-motion and historical data at-rest, enabling complex analytical and transactional workloads. The platform supports deployment across on-premises, public cloud, hybrid-cloud, and multi-cloud environments. GridGain is widely used in industries such as financial services, telecommunication, transportation, and logistics for real-time risk management, smart decisioning, and high-performance online transactional processing (OLTP), among other use cases.
In-Memory Computing • In-Memory Data Grid • Apache Ignite • Stream Processing • High Performance Transaction Processing
Senior Software Engineer - Java
December 11, 2024
GridGain
GridGain is a Unified Real-Time Data Platform created by the original developers of Apache Ignite. It provides enterprises with a simplified and highly optimized data architecture that supports extreme speed, massive scale, and high availability for their data ecosystems. GridGain combines stream processing, a distributed in-memory data grid, and colocated compute to deliver data processing and analytics with ultra-low latencies. It is designed to seamlessly integrate both streaming data in-motion and historical data at-rest, enabling complex analytical and transactional workloads. The platform supports deployment across on-premises, public cloud, hybrid-cloud, and multi-cloud environments. GridGain is widely used in industries such as financial services, telecommunication, transportation, and logistics for real-time risk management, smart decisioning, and high-performance online transactional processing (OLTP), among other use cases.
In-Memory Computing • In-Memory Data Grid • Apache Ignite • Stream Processing • High Performance Transaction Processing
📋 Description
• Develop and optimize data structures for best experience accessing data in high data-intensive scenarios • Implement the best way to store data to disk and send data via network to other nodes (including failure handling and recovering) • Implement and integrate algorithms for high availability of the cluster • Investigate flaws in data consistency algorithms, requiring thorough debugging on multi-node cluster and in low-level byte represented data
🎯 Requirements
• 4+ years experience in Java programming • Deep knowledge of concurrency in Java and Java Memory Model and/or of concurrency model in other programming systems • Excellent Java SE knowledge including IO, JVM internals, etc. • Experience with SQL databases • Experience in troubleshooting Java applications and/or databases • English — upper-intermediate or higher • Experience with NoSQL and/or distributed databases (strong plus) • Experience with building highload distributed systems and algorithms (strong plus) • Experience with Docker, Kubernetes and/or public Cloud (AWS, GCE, Azure, etc.) (strong plus)
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