Python & AWS Software Engineer III

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.
ife 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.
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 and UI technologies.
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.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Overall knowledge of the Software Development Life Cycle.
Solid understanding of agile methodologies such as CI/CD, application resiliency, and security.
Preferred qualifications, capabilities, and skills
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).
Familiarity with modern front-end technologies such as React, TypeScript, HTML/CSS.
Familiarity with data pipeline and working with large-scale datasets. R distributed systems (e.g., Apache Kafka) in production environments (good to have).
Experience with observability tooling (Datadog, Splunk, Dynatrace, Grafana).