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

JPMorgan Chase & Co.•Jersey City, NJ

17h•$171,000 - $260,000

About The Position

As a Senior Lead Site Reliability Engineer at JPMorgan Chase 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. 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.

Requirements

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

Nice To Haves

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

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