To be successful in this role, candidates should demonstrate:
- Strong expertise in designing and developing low-latency, high-throughput backend systems using Java, with experience implementing event-driven architectures leveraging Kafka or similar messaging technologies.
- Deep understanding of real-time stream processing concepts, including event ordering, partitioning, replay mechanisms, idempotency, fault tolerance, and delivery semantics.
- Hands-on experience with the Kafka ecosystem, including producers, consumers, topics, schema management, Kafka Streams, and the development of scalable real-time data pipelines.
- Proven experience designing and optimizing distributed systems and microservices architectures, including performance tuning, caching strategies, and technologies such as Redis.
- Strong software engineering capabilities in both Java and Python, with experience building hybrid architectures that combine real-time streaming workloads and large-scale distributed data processing.
- Experience developing platforms that support risk analytics, margin calculations, exposure monitoring, or other time-sensitive financial computations.
- Exposure to AI/ML-driven applications, including predictive analytics, forecasting, anomaly detection, or optimization use cases.
- Strong experience with AWS cloud services and cloud-native application development, including containerization and orchestration technologies such as Kubernetes.
- Ability to design and support highly available, resilient, multi-region architectures capable of scaling to demanding real-time workloads.
- Commitment to engineering excellence through sound system design, clean coding standards, CI/CD practices, DevSecOps methodologies, observability, and data-driven engineering metrics.
- Experience implementing automated testing strategies and quality engineering practices that ensure production-grade reliability and maintainability.
- Familiarity with modern AI-assisted software development tools and their application to accelerating development, testing, and operational efficiency.
Ideal Candidate Background
Candidates who are likely to be a strong fit will typically come from environments where they have built or supported:
- Prime Brokerage Risk Platforms
- Margin and Collateral Management Systems
- Market Risk Platforms
- Credit Risk Systems
- Real-Time Trading or Post-Trade Processing Platforms
- Front Office Analytics Platforms
- Low-Latency Financial Services Applications
- Event-Driven Data Streaming Architectures
EOE Statement: Specialist Staffing Group is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
In addition to base pay, direct-hire employees may be eligible for client offered benefits such as medical, dental, and vision coverage, and paid leave where required by applicable law. Eligibility may vary based on factors such as location and hire date and is subject to change.
To find out more about Huxley, please visit www.huxley.com
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