Senior Java Developer

We are

At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron’s progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile-first applications and more. Over the last 20+ years, our company has been honored with multiple employer awards, recognizing our commitment to our talented teams. With top clients to boast about, Synechron has a global workforce of 14,500+, and has 58 offices in 21 countries within key global markets.

Our challenge

We are building Polaris, a next-generation front-to-back platform for post-trade processing, real-time risk monitoring, and cloud-native orchestration. We are seeking a hands-on, high-impact Java developer with a proven track record in designing and operating large-scale distributed systems. Candidate will contribute to the architecture, implementation, and operations of a low-latency, high-throughput trading data path, ensuring scalability, resiliency, recoverability, observability, and developer productivity.

Additional Information*

The base salary for this position will vary based on geography and other factors. In accordance with law, the base salary for this role if filled within Alpharetta, GA is $115k - $125k/year & benefits (see below).

The Role

Responsibilities:

• Design, 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.

Requirements:

• 8+ 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).

Preferred, but not required:

• Experience with front-to-back architectural patterns in trading or financial services.
• Knowledge of, or experience with, RocksDB, distributed caches, and columnar stores.
• Familiarity with t