Software Engineer II Python/Observation

JPMorgan Chase & Co.•Jersey City, NJ

15h•$123,500 - $150,000

About The Position

As a Software Engineer II at JPMorganChase within the AI/ML Data Platforms, you are part of an agile team that works to enhance, design, and deliver the software components of the firm’s state-of-the-art technology products in a secure, stable, and scalable way. 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.

Requirements

Formal training or certification on software engineering concepts and 2+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability
Experience in Python developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
Experience in observability such as white and black box monitoring, service level objective alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, and others
Strong understanding of SLI/SLO/SLA and Error Budgets
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.
Experience across the whole Software Development Life Cycle
Exposure to agile methodologies such as CI/CD, Application Resiliency, and Security

Nice To Haves

Experience in Data pipelines using Spark
Exposure to AWS & Databricks Platform administration.
Knowledge of containerization (Docker, Kubernetes) and orchestration.

Responsibilities

Executes standard software solution, design, development, and technical troubleshooting
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.
Designs, develops, codes, and troubleshoots with consideration of upstream and downstream systems and technical implications
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
Applies technical troubleshooting to breakdown solutions and solve technical problems of basic complexity
Gathers, analyzes, and draws conclusions from large, diverse data sets to identify problems and contribute to decision-making in service of secure, stable application development
Learns and applies system processes, methodologies, and skills for the development of secure, stable code and systems