Sr Lead Software Engineer - Python, Athena

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As a Lead Site Reliability Engineer at JPMorgan Chase within the Commercial & Investment Bank – Markets – Commodities Technology, you hold a technical leadership role in your team, demonstrate strong knowledge across multiple technical domains, and advise others on the technical and business issues facing them. You will will set the vision, strategy, and operating model for our SRE transformation—enabling our business-aligned support teams to deliver higher reliability, stronger resilience, and a measurably better end-user experience across the board. 

Job responsibilities

• Defines the SRE vision, north-star outcomes, and multi-year roadmap for the Production Management team, aligned to both CIB and JPM Global Technology priorities.

• Establishes the SRE operating model across global regions (ways of working, intake, prioritization, engagement with engineering teams and production support).

• Partners with business-aligned Production Support leads to embed SRE practices consistently and act as a force multiplier – coaching them on reliability thinking, prioritization, and “engineering out” operational load.

• Builds and develops a small, high-impact core SRE team (and/or virtual SRE community of practice) that scales reliability improvements across many application flows.

• Defines and implements standards for: Service cataloging, SLO/SLI frameworks and error budgets, incident response maturity, blameless post-incident reviews, resiliency patterns, capacity, performance, and scalability engineering.

• Drives service reviews with evidence-based reporting (availability, latency, incident trends, MTTR/MTTD, change failure rate, customer impact).

• Champions AI adoption and deliver AI-enabled capabilities to reduce operational toil and improve speed/quality of response.

• Sets direction for observability across logs/metrics/traces, including instrumentation standards, golden signals, and end-user journey monitoring. Improves alert quality and routing: reduce false positives, improve actionable alerts, and tighten feedback loops to engineering teams.

• Builds strong partnerships with application development teams, platform/infrastructure partners, and governance functions. Communicates clearly and credibly at all levels—from engineers to senior technology and business stakeholders.

• Uses enterprise-authorized AI capabilities within the work environment to accelerate major-incident triage, troubleshooting, and post-incident analysis, validating outputs and handling operational data according to sensitivity and security requirements.

• Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., CI/CD quality checks, test/validation automation, and operational readiness), ensuring traceability/auditability, resiliency, and security controls.

Required qualifications, capabilities, and skills

• 10+ years of experience in technology support, production/application support, DevOps, or infrastructure management.

• Demonstrated experience leading SRE/reliability engineering or production engineering transformations in a complex enterprise environment.

• Strong engineering background: ability to design, build, and deliver automation and reliability solutions.

• Fluency & expertise in Python

• Deep practical knowledge of: SLOs/SLIs, error budgets, incident management, postmortems, observability design across metrics/logs/traces and distributed systems troubleshooting, resilience engineering, performance/capacity management, and change risk reduction.

• Proficiency and experience with telemetry (logs/metrics/traces) collection using tools and standards such as Prometheus, OpenTelemetry, Datadog, Dynatrace, Splunk.

• Experience delivering automation at scale (scripting, workflow automation, runbook automation, CI/CD-integrated guardrails).

• Proven leadership skills: influencing without authority, coaching leaders, and building communities of practice.

• Strong judgment around risk, security, and controls—especially when applying AI to production workflows.

• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to improve SRE workflows (e.g., incident investigation support and knowledge capture) with strong validation habits and awareness of data sensitivity.

• Ability to evaluate AI-assisted operational recommendations for correctness and risk, define appropriate guardrails for team usage, and ensure outcomes align to resiliency and security expectations.

Preferred qualifications, capabilities, and skills