Job responsibilities

• Manage day-to-day execution of SRE functions (workload prioritization, shift coverage, triage quality, escalations, runbooks, and handoffs) to ensure consistent and timely outcomes during market hour
• Drives reuse-first adoption of enterprise-authorized AI capabilities within the work environment to improve reliability operations and customer experience outcomes, with human-in-the-loop validation and appropriate handling of sensitive data.
• Provide North America leadership for  production management teams supporting trading desks across multiple Markets lines of business; ensure reliable day-to-day operations and sustained stability improvements
• Lead and coordinate L1/L2 investigations and incident response; ensure clear ownership, high-quality communications, and follow-through to root cause and prevention
• Act as a key technology partner to the trading desks: monitor operational signals, drive rapid engagement, translate business impact into technical action, and communicate clearly under pressure
• Drive adoption of SRE practices across delivery teams, ensuring best practices are implemented and demonstrated empirically via stability and reliability metrics (e.g., SLOs, error budgets, incident trends)
• Own and evolve observability (dashboards/alerts/SLOs, instrumentation, monitoring strategy) and use data to prioritize resiliency, performance, and scalability improvements
• Deliver automation and tooling that reduces operational toil and improves support effectiveness (faster diagnosis, safer remediation, repeatable fixes, and self-service workflows)
• Establish and enforce operational standards for delivery teams (operational readiness, testing discipline, release safety, rollback strategy, post-incident actions) and hold teams accountable for closing gaps
• Establishes team standards for AI-assisted reliability workflows across automation and delivery practices, ensuring traceability/auditability, resiliency, and security controls.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience . In addition, 2 + years of experience leading technologists to manage and solve complex technical items within your domain of expertise
• Demonstrated experience supporting front-office / trading desk workflows or similarly time-sensitive production environments, with comfort operating during market hours
• Proven production management / SRE leadership experience (support rotations, incident response, root cause analysis, post-incident actions, reliability improvements)

• Experience leading teams in the safe use of enterprise-authorized AI capabilities within the work environment for reliability engineering workflows, including validation habits and awareness of data sensitivity.\

• Ability to set and reinforce organization-level practices for reviewing AI-assisted recommendations and escalating uncertain decisions while maintaining resiliency, security, and auditability outcomes.

• Experience leading technologists in a player/coach capacity, including guiding support staff and influencing senior engineers and delivery teams
• Strong engineering fundamentals: distributed systems thinking, debugging, performance analysis, and pragmatic tradeoffs under pressure
• Practical AWS experience supporting production services (troubleshooting, deployment, operational visibility)
• Strong stakeholder management and communication skills, especially during incidents and high-pressure periods
• Proficiency in at least one programming language (e.g., Python, Java/Spring Boot, .NET) and ability to automate/engineer solutions that reduce toil

Preferred qualifications, capabilities, and skills

• Ability to code and demonstrate data fluency
• Prior Markets experience (Fixed Income preferred; experience supporting multiple lines of business is a plus)
• Hands-on experience with AWS CLI, CloudWatch, and cloud-native operational patterns
• Experience with Datadog (metrics/logs/traces, alert tuning, SLOs) and/or comparable observability stacks
• Experience applying AI-assisted tooling to reduce operational toil (e.g., incident summarization, runbook assistance), with appropriate controls and governance. 
• Coach and develop engineers through individualized mentoring; ensure knowledge is documented and shared via internal forums and communities of practice