Our client is a globally recognized quantitative trading firm investing heavily in the next generation of its data platform. As data volumes continue to grow across research and trading, they're expanding the engineering team responsible for building the infrastructure that powers real-time analytics, machine learning, and production trading systems. This role offers the opportunity to work on large-scale distributed data platforms where reliability, performance, and scalability are critical. You'll partner closely with software engineers, quantitative researchers, and infrastructure teams to design systems that process enormous volumes of market and business data with minimal latency What you'll Design and build scalable data infrastructure supporting real-time and batch workloads Develop streaming and distributed data pipelines using modern Big Data technologies Improve performance, reliability, and observability across production data systems Work with engineering teams to optimize data architecture, storage, and ingestion strategies Troubleshoot production issues and drive long-term improvements to platform stability Contribute to the evolution of the firm's data ecosystem and engineering best practices Looking For Strong experience building production data platforms in distributed environments Hands-on experience with Kafka, Spark, Flink, or similar streaming technologies Strong programming skills in Python, Java, or SQLExperience working with modern data storage technologies (Iceberg, Databricks, S3, HDFS, etc.)Familiarity with Kubernetes, Docker, and cloud-native infrastructure Solid understanding of Linux environments and production operations Strong analytical mindset with the ability to investigate and resolve complex data issues
