Description
We are seeking a Head of Credit Production Engineering to lead the Global Credit Production Engineering across FIC Technology, a globally distributed team. You will drive global production stability across the Credit business, aligning engineering priorities with business objectives, regulatory expectations, and platform resilience goals.
Responsibilities
- Drive global production stability across the Credit business, aligning engineering priorities with business objectives, regulatory expectations, and platform resilience goals
- Embed modern Site Reliability Engineering (SRE) principles and lead the transformation from reactive support to a proactive, data-driven, automation-first engineering discipline
- Enhance platform robustness by challenging and reviewing architectures, contributing to tooling strategy, and role-modelling excellence in incident response, post-incident analysis, and risk reduction
Leadership
- Act as a senior partner to the Head of Credit Trading, Credit desk leads, technology executives, and adjacent business stakeholders, ensuring transparency, aligned priorities, and shared accountability for platform performance and health
- Develop, mentor, and scale a high-performing global SRE-aligned team, fostering a culture of ownership, technical excellence, curiosity, and continuous improvement
Requirements
- Expertise in incident management, post-mortem and problem-management culture, change governance, and operating within regulated financial environments
- Advanced technical proficiency in Linux/Unix systems, Structured Query Language, and programming in C++, Java, Python, or equivalent scripting languages; strong understanding of distributed systems, low-latency architectures, APIs and event-driven architecture
- Hands-on experience with observability and telemetry platforms (e.g., Prometheus, Grafana, Splunk, Geneos, OpenTelemetry) and infrastructure automation (e.g., Ansible, Terraform, continuous integration/continuous deployment pipelines)
- Familiarity with hybrid cloud, containerization, Kubernetes, Docker, and modern deployment methodologies
- Proven ability to leverage AI tools to enhance productivity, optimize workflows to solve business problems, while applying critical judgment to ensure responsible and ethical use of data and AI outputs