Senior Lead Site Reliability Engineer - Manager-AI/ML and Data Platforms

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 130,000 - 160,000; Full time

Job summary

JPMorgan Chase & Co. is looking for a Senior Lead Site Reliability Engineer in Jersey City, New Jersey. This role involves working with the Chief Data & Analytics Office to define non-functional requirements for large-scale data platforms. The ideal candidate will have extensive experience in site reliability engineering and will be key in driving the adoption of AI in reliability workflows.

Responsibilities include designing systems, mentoring technologists, and implementing observability designs. Applicants should have formal training in site reliability concepts and advanced knowledge of monitoring tools such as Grafana, Prometheus, and Datadog.

Qualifications

• 5+ years applied experience in site reliability engineering.
• Strong understanding of SLI/SLO/SLA and error budgets.
• Ability to communicate data-based solutions effectively.

Responsibilities

• Design high-quality system designs and roadmaps.
• Mentor engineers on technical and business issues.
• Implement observability and reliability designs for systems.

Skills

Site reliability engineering concepts

Observability

Advanced knowledge of SLI/SLO/SLA

Python programming

AI capabilities for reliability workflows

Education

Certification in site reliability engineering

Tools

Grafana

Prometheus

Datadog

Kubernetes

Job description

Elevate your engineering prowess to unprecedented levels by joining a team of exceptionally gifted professionals and position yourself among the top echelon in site reliability. As a Senior Lead Site Reliability Engineer at JPMorgan Chase within the Chief Data & Analytics Office (CDAO) AI/ML & Data Platforms team, you work with your fellow stakeholders to define non-functional requirements (NFRs) and availability targets for services supporting large-scale data platforms and data lake ecosystems. You will ensure those NFRs are embedded into product design and testing phases, that service level indicators effectively measure customer and data platform performance, and that service level objectives are defined with stakeholders and implemented in production to support secure, scalable, and high-performing analytics and AI/ML workloads.

Job responsibilities

• Creates and delivers high quality designs, roadmaps, and program charters alongside the engineering teams, including data platform and distributed systems initiatives
• Acts as a key resource and mentor for technologists in your area seeking advice on technical and business issues, and serves as a culture carrier and site reliability adoption champion for your team
• Collaborates with others to create and implement observability and reliability designs for complex systems and data platforms which are robust, stable, and do not incur additional toil or technical debt
• Uses enterprise-authorized AI capabilities within the work environment to accelerate reliability design and operational decisioning (e.g., incident/post-incident analysis and requirements traceability), validating outputs and handling operational data according to sensitivity and security requirements
• Drives evolution and debugging of critical components, including platform and data system dependencies, by understanding application and infrastructure interdependencies and limitations
• Provides comprehensive and ongoing guidance, tools, and solutions to support the firm’s growth, including scalable data platform infrastructure and engineering best practices
• Makes significant contributions to JPMorganChase’s site reliability community via internal forums, communities of practice, guilds, and conferences
• Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., testing/validation automation and production readiness), ensuring traceability/auditability, resiliency, and security controls across application and data platform environments

Required qualifications, capabilities, and skills

• Formal training or certification on site reliability engineering concepts and 5+ years applied experience
• Brings an advanced understanding of site reliability culture and principles and a track record of demonstrating how to implement site reliability within applications, platforms, or large-scale data systems, including strong understanding of SLI/SLO/SLA and error budgets
• Advanced knowledge and experience in observability such as white and black box monitoring, service level objectives, alerting, and telemetry collection across distributed and data platform environments, including tools such as Grafana, Dynatrace, Prometheus, Datadog, and Splunk
• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to improve reliability engineering workflows with strong validation habits and awareness of data sensitivity
• Ability to set team practices for safe AI usage in operations (e.g., review/approval expectations and escalation paths) while maintaining resiliency, security and auditability outcomes, ensuring compliance with risk controls and company-wide standards
• Advanced knowledge of software applications and technical processes, including distributed systems, system design, resiliency, testing, operational stability, and disaster recovery, with considerable depth in one or more technical disciplines
• Demonstrated ability to communicate data-based solutions with complex reporting and visualization methods and collaborate effectively across teams to drive incident resolution and improvements
• Recognized as an active contributor of the engineering community
• Strong communication skills and a desire to mentor and educate others on site reliability engineering principles and practices while building strong cross‑functional relationships

Preferred qualifications, capabilities, and skills

• Experience with AWS platforms and managed data platforms such as Databricks, including platform administration and engineering support
• Experience in building and managing data pipelines using Spark or similar distributed compute frameworks
• Familiarity with big data ecosystem tools (e.g., Spark, Glue, MapReduce)
• Knowledge of containerization (Docker, Kubernetes) and orchestration frameworks
• Experience with CI/CD pipelines, automation frameworks, and infrastructure as code (e.g., Terraform)
• Proficiency in Python or similar programming languages for automation and platform development
• Familiarity with large-scale distributed systems and data processing environments