My client is a leading multi-strategy quantitative hedge fund anchored in technology-driven research and approaches; they focus on systematic trading across global markets. Their edge comes from a deep commitment to innovation building proprietary systems, developing cutting-edge infrastructure, and investing in the brightest minds in the industry.
They see data as raw material the foundation upon which everything they build. They are building a modern data infrastructure to power real-time decision-making. The systems handle massive volumes of streaming data with strict requirements around latency, reliability, and correctness. A Data Engineer is sought who enjoys working at the intersection of stream processing, cloud infrastructure, and data quality.
What You'll Own
Streaming Infrastructure
- Develop and maintain high-throughput data pipelines that process event streams in real time
- Work with Apache Flink to build reliable stream processing applications, applying best practices around event-time handling, state management, and fault tolerance
- Tune pipeline performance to handle growing data volumes while maintaining low latency
Cloud & Operations
- Manage cloud-based infrastructure supporting streaming workloads
- Implement infrastructure-as-code practices to ensure repeatable and scalable deployments
- Monitor system health, set up alerting, and respond to production incidents with a focus on root cause resolution
Data Quality & Tooling
- Build frameworks to validate data quality at scale, handling schema changes gracefully
- Create observability dashboards to track pipeline SLIs such as throughput, latency, and error rates
What You Bring
Core Experience
- 3+ years of experience building and operating real-time data systems
- Familiarity with stream processing frameworks Apache Flink or Spark is preferred
- Proficient in Python, including experience working with data processing libraries
- Solid SQL skills, particularly in the context of streaming or analytical databases
- Experience with cloud environments, including services such as managed compute, object storage, and message brokers
- Understanding of containerization and orchestration tools (Docker, Kubernetes)
- Familiarity with monitoring and observability tools (e.g. Grafana, CloudWatch) and experience troubleshooting production systems
