Software Engineer II - Observability Engineering

JPMorgan Chase & Co.•Columbus, OH

About The Position

As a Software Engineer II – Observability Engineer at JPMorganChase within Asset & Wealth Management Technology, you are part of an agile team that works to design and deliver monitoring and observability solutions that enables high-quality telemetry and operational resilience. As an emerging member of a software engineering team, you execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role. As an Observability Engineer, you will help design, implement, and optimize observability solutions that provide actionable insights into application health, performance, and operational effectiveness. You will work closely with software engineers, platform teams, product teams, and technology stakeholders to improve telemetry quality, monitoring coverage, dashboarding, alerting, and operational visibility across Asset & Wealth Management applications. This role is ideal for an engineer who is passionate about monitoring, telemetry, analytics, automation, cloud technologies, and operational intelligence. You will contribute to strategic observability initiatives that help engineering teams gain deeper visibility into their platforms while improving reliability, performance, and customer experiences.

Requirements

Formal training or certification on software engineering concepts and 2+ years applied experience.
Experience in Platform Engineering, DevOps, Monitoring, or Observability-related roles.
Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages such as Python, Java, JavaScript, Go, or similar technologies.
Experience with one or more observability or monitoring platforms including: Dynatrace, Splunk, Grafana, and/or Datadog.
Experience working with telemetry concepts including metrics, logs, traces, and alerting.
Knowledge of cloud platforms, distributed systems, and modern application architectures.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.
Exposure to agile methodologies such as CI/CD, Application Resiliency, and Security.
Strong analytical, troubleshooting, and problem-solving capabilities.
Strong verbal and written communication skills and the ability to collaborate across engineering teams.

Nice To Haves

Knowledge of monitoring standards, telemetry governance, and observability frameworks.
Experience with automation, Infrastructure as Code, or platform engineering practices.
Exposure to AI-assisted analytics, anomaly detection, or operational intelligence capabilities.
Experience building dashboards, observability reports, and operational analytics solutions.

Responsibilities

Configure and optimize monitoring, alerting, and event management; reduce alert noise and improve monitoring hygiene, while driving platform adoption and enabling self-service through automation, observability-as-code, integrations, and AI-enabled capabilities.
Partner with application teams to instrument services and standardize telemetry (metrics, logs, traces, events) and alerting through consistent tagging, patterns, and data quality practices.
Build and run monitoring/observability solutions across cloud and on-premises environments, improving visibility into system health, business transactions, and customer experience.
Deliver operational insights via dashboards, visualizations, and analysis to understand user journeys and dependencies, detect performance/service degradation, and accelerate issue identification.
Executes standard software solution, design, development, and technical troubleshooting.
Designs, develops, codes, and troubleshoots with consideration of upstream and downstream systems and technical implications.
Writes secure and high-quality code using the syntax of at least one programming language with limited guidance.
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.