Job TitleDesign, implement, and maintain high-performance, low-latency messaging and processing components (Java-based) for real-time trading data.Develop and operate resilient microservices and streaming pipelines using Kafka and related technologies.Build and optimize storage and access patterns with modern databases (e.g., RocksDB, MongoDB, MemSQL/SingleStore) for real-time analytics.Lead efforts to improve SDLC, testing (shift-left), configuration, and developer experience (CI/CD, automation, tooling).Architect and implement observability frameworks: metrics, tracing (Tempo/Jaeger), logging, and alerting for highly available systems.Collaborate with Platform/Cloud-native teams to design Kubernetes-based deployments, scalability, and disaster recovery strategies.Implement backpressure, idempotency, retries with exponential backoff and jitter, circuit breakers, and fault-tolerant patterns.Provide technical leadership, mentorship, and code reviews; communicate effectively with stakeholders across teams.Participate in on-call rotations and incident response, driving root-cause analysis and post-mortems.Requirements8+ years of professional software development, with a strong emphasis on Java and distributed systems.Deep hands-on experience building large-scale, low-latency, high-throughput systems.Expertise with messaging and streaming technologies, especially Kafka (producers/consumers, transactions, exactly-once processing).Proficiency with data stores used in real-time contexts (RocksDB, MongoDB, MemSQL/SingleStore) and time-series considerations.Solid understanding of concurrency, memory models, locking vs. lock-free approaches, and performance tuning in Java.Experience designing and operating in Kubernetes-based environments; familiarity with cloud-native patterns, DevOps, and observability tooling.Strong problem-solving, debugging, and performance profiling skills.Excellent communication, collaboration, and leadership capabilities.Bonus: Python scripting experience; knowledge of Prometheus/Grafana, Tempo/Jaeger, and cloud platforms (AWS/GCP/Azure).Nice-to-HaveExperience with front-to-back architectural patterns in trading or financial services.Knowledge of, or experience with, RocksDB, distributed caches, and columnar stores.Familiarity with time-series data modeling and windowed computations.Exposure to risk monitoring, market data feeds, and post-trade workflows.