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

About the role

Elevate your engineering prowess by joining a team of exceptionally gifted professionals within the Chief Data & Analytics Office (CDAO) AI/ML & Data Platforms team. You will work with stakeholders to define non-functional requirements (NFRs) and availability targets for services supporting large-scale data platforms and data lake ecosystems. Ensure NFRs are embedded into product design and testing phases, service level indicators measure customer and data platform performance, and service level objectives are defined with stakeholders and implemented in production to support secure, scalable, and high-performing analytics and AI/ML workloads.

Responsibilities

• Creates and delivers high-quality designs, roadmaps, and program charters alongside engineering teams, including data platform and distributed systems initiatives.
• Acts as a key resource and mentor for technologists seeking advice on technical and business issues, serving as a culture carrier and site reliability adoption champion.
• Collaborates to create and implement observability and reliability designs for complex systems and data platforms that are robust, stable, and do not incur additional toil or technical debt.
• Uses enterprise-authorized AI capabilities 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.

Requirements

• Formal training or certification on site reliability engineering concepts and 5+ years of applied experience.
• Advanced understanding of site reliability culture and principles, with a track record of implementing site reliability within applications, platforms, or large-scale data systems, including strong knowledge of SLI/SLO/SLA and error budgets.
• Advanced knowledge and experience in observability, including white and black box monitoring, service level objectives, alerting, and telemetry collection across distributed and data platform environments (e.g., Grafana, Dynatrace, Prometheus, Datadog, Splunk).
• Demonstrated experience using enterprise-authorized AI capabilities 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 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 depth in one or more technical disciplines.
• Demonstrated ability to communicate data-based solutions using complex reporting and visualization methods and collaborate effectively across teams to drive incident resolution and improvements.
• Recognized as an active contributor to the engineering community.
• Strong communication skills and a desire to mentor and educate others on site reliability engineering principles while building cross-functional relationships.

Preferred Qualifications

• 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.

Benefits

• Comprehensive health care coverage.
• On-site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.

Pay

Competitive total rewards package including base salary determined based on the role, experience, skill set, and location. Eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.

About Us

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses, and many of the world's most prominent corporate, institutional, and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years, and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing, and asset management.