Quantitative Support Engineer - Selby Jennings
You will work with Linux, AWS, Docker, Kubernetes, CI/CD pipelines, Python, Bash, and monitoring tools like Datadog, Prometheus, CloudWatch, or ELK. You will support and maintain the critical research and trading infrastructure for a quantitative hedge fund, acting as first-line support for platform-related issues.
Free Tailor for ATS: 10/10 runs left
Quantitative Support Engineer
Our client is an exceptional hedge fund at the forefront of quantitative research and technology, seeking a Quantitative Support Engineer to join a highly visible platform team. This role sits at the intersection of trading, quantitative research, and engineering, withresponsibilityforsupportingandmaintainingthefirm'scriticalresearchandtradinginfrastructure.Youwillactasthefirstlineofsupportforplatform-relatedissues,ensuringthereliability,stability,andperformanceofsystemsthatunderpininvestmentdecisions.
They are looking for a proactive and technically curious engineer with a strong troubleshooting mindset and a genuine sense of ownership. The ideal candidate will be comfortable operating in a fast-paced environment, communicatingwithbothtechnicalandnon-technicalstakeholders,andsolvingcomplexinfrastructureissueswithacalm,methodicalapproach.Someonewhoenjoysimprovingprocesses,automatingrepetitivetasks,andworkingcloselywithtraders,quants,andengineerswillexcelinthisrole.
Key Responsibilities
Act as the first point of contact for infrastructure and platform-related issues across research and trading environments
Monitor platform health, alerts, logs, and dashboards to proactively identify and respond to incidents
Investigate, troubleshoot, and resolve operational issues to minimise downtime and business impact
Manage user onboarding, permissions, environment setup, and resource allocation requests
Drive continuous improvements to monitoring, automation, documentation, and support processes
Key Skills & Experience
Strong Linux systems administration and troubleshooting experience within production environments
Solid understanding of networking fundamentals and cloud platforms, preferably AWS
Experience with monitoring and observability tools such as Datadog, Prometheus, CloudWatch, or ELK
Knowledge of Docker, Kubernetes, CI/CD pipelines, and scripting with Python or Bash
Excellent communication skills, a structured problem-solving mindset, and the ability to perform under pressure in fast-paced environments such as trading, quantitative research, or financial services sectors