Software Engineer III -Java Full Stack

JPMorganChase•Plano, TX

About The Position

As a Software Engineer III in JPMorgan Chase’s CTO – Identity & Access Management (IAM) organization, you will be a seasoned member of an agile team designing and delivering secure, stable, scalable technology products. You will build and operate an AWS-based data platform and control plane that automates cloud infrastructure provisioning, platform configuration, and data pipeline enablement, including the Databricks-on-AWS implementation. The role delivers critical technology solutions across multiple technical areas in support of firm objectives. It calls for a curious engineer who thrives in ambiguity, learns quickly, and delivers high-quality outcomes, using enterprise-authorized AI tools responsibly to improve productivity and business value. You will develop and maintain a Python-based control plane, secure backend services and APIs, and infrastructure-as-code automation. You will produce architecture and design artifacts, ensure software meets security, resiliency, scalability, and cost constraints, and improve reliability and observability. The role partners with IAM/security, data engineering, and application teams to integrate platform controls and enable secure analytics at scale.

Requirements

Formal training or certification in software engineering concepts and at least 3 years of applied experience.
Hands-on practical experience in system design, application development, testing, and operational stability.
Strong experience developing, debugging, and maintaining production code in a large corporate environment using Python (preferred) or another modern programming language, as well as UI technologies.
Strong AWS and infrastructure fundamentals, including services such as IAM, KMS, S3, and CloudWatch; compute or orchestration services such as EKS, ECS, or Lambda where applicable.
Hands-on infrastructure-as-code experience, preferably Terraform; CloudFormation is acceptable.
Experience with automation-first engineering.
Solid understanding of the SDLC, CI/CD, automated testing, and safe deployment practices.
Hands-on experience with enterprise-authorized AI-assisted software development tools, including critically evaluating, validating, and refining generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity, secure input and output handling, and security and resiliency expectations; ability to guide peers on safe and effective use.
Solid understanding of agile methodologies, application resiliency, and security.

Nice To Haves

Hands-on experience with Databricks on AWS or similar data engineering and analytics platforms, such as Spark-based environments or data lake/lakehouse platforms, and related jobs, orchestration, cluster/runtime, or governance/catalog components.
Familiarity with React, TypeScript, HTML, and CSS.
Familiarity with data pipelines and large-scale datasets.
Production experience with streaming or distributed systems such as Apache Kafka.
IAM domain knowledge, including authentication, authorization patterns, and RBAC/ABAC concepts.
Experience with observability tools such as Datadog, Splunk, Dynatrace, or Grafana.
Kubernetes and Docker familiarity.

Responsibilities

Execute software solutions across design, development, and technical troubleshooting, applying approaches beyond routine or conventional solutions.
Create secure, high-quality production code and maintain services and automation that support AWS data platform provisioning and pipeline enablement.
Design, develop, and troubleshoot a Python-based control plane for platform provisioning, configuration, and pipeline enablement, including Databricks on AWS.
Build secure, reliable backend services and APIs for self-service workflows such as onboarding, workspace and environment provisioning, policies and guardrails, job orchestration hooks, environment configuration, and secrets and access patterns.
Implement infrastructure-as-code and automation for AWS platform components, including identity controls, encryption, networking integrations, logging, monitoring, and guardrails.
Use enterprise-authorized AI coding tools to improve code quality, delivery speed, and productivity; validate AI-generated work through peer review, automated testing, and secure coding standards, and contribute reusable practices to the team.
Use SDLC tools, including authorized AI-assisted development and automation capabilities, to improve the value delivered by automation.
Produce architecture and design artifacts for complex applications and ensure implementation meets security, resiliency, scalability, and cost constraints.
Drive reliability and observability practices, including logs, metrics, tracing, retries, idempotency, timeouts, and runbooks, and continuously improve platform operations.
Partner with IAM/security, data engineering, and application teams to integrate platform controls and enable secure analytics at scale.
Identify patterns and hidden problems in operational data and use findings to improve coding hygiene and system architecture.