We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Software Engineer III at JPMorgan Chase within the CTO – Identity & Access Management (IAM) organization, you serve as a seasoned member of an agile team to design and deliver trusted, market-leading technology products in a secure, stable, and scalable way. You will build and operate an AWS-based data platform and control plane that automates provisioning of cloud infrastructure, platform configuration, and data pipeline enablement (including our Databricks-on-AWS implementation). You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. 

We’re looking for a curious engineer who thrives in ambiguity, learns quickly, and delivers high-quality outcomes—leveraging enterprise-authorized AI tools responsibly to improve productivity and drive business value

Job responsibilities

• Execute software solutions across design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
• Create secure and high-quality production code and maintain services/automation that support data platform provisioning and pipeline enablement on AWS.
• Design, develop, and troubleshoot software solutions for a Python-based control plane that automates platform provisioning, configuration, and pipeline enablement (including Databricks-on-AWS).
• Build secure, reliable backend services and APIs that enable self-service workflows (e.g., onboarding, workspace/environment provisioning, policy/guardrails, job orchestration hooks, environment configuration, secrets/access patterns).
• Implement infrastructure-as-code and automation for AWS platform components (identity controls, encryption, networking integrations, logging/monitoring, guardrails).
• Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contribute learnings and reusable patterns to improve broader team effectiveness.
• Apply 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.
• Produce architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development (security, resiliency, scalability, cost).
• Drive reliability and observability practices (logs/metrics/tracing, retries, idempotency, timeouts, 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.
• Proactively identify hidden problems and patterns in operational data and use these insights to drive improvements to coding hygiene and system architecture.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 3+ years 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 with Python  or another modern programming language.
• Familiarity with modern front-end technologies such as React, TypeScript, HTML/CSS.
• Strong experience with AWS and infrastructure fundamentals, including services such as IAM, KMS, S3, CloudWatch (and compute/orchestration such as EKS/ECS/Lambda where applicable).
• Hands-on experience with infrastructure-as-code and automation-first engineering.
• Solid understanding of the SDLC, including CI/CD, automated testing, and safe deployment practices.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.

Preferred qualifications

• Hands-on experience with Databricks on AWS or similar data engineering / analytics platforms (e.g., Spark-based environments, data lake/lakehouse platforms) and ecosystem components (jobs/orchestration, cluster/runtime concepts, governance/catalog concepts).
• Experience with streaming or distributed systems (e.g., Apache Kafka) in production environments (good to have).
• IAM domain knowledge (Auth, authorization patterns, RBAC/ABAC concepts).
• Experience with observability tooling (Datadog, Splunk, Dynatrace, Grafana).