Python & AWS Software Engineer III
About The Position
As a Software Engineer III at JPMorgan Chase in the CTO – Identity & Access Management (IAM) organization, you will serve as a seasoned member of an agile team designing and delivering secure, stable, and 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 technology solutions across multiple technical areas in support of the firm’s business objectives. It calls for a curious engineer who can work through ambiguity, learn quickly, and deliver high-quality outcomes while responsibly using enterprise-authorized AI tools to improve productivity and business value.
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 or another modern programming language and UI technologies.
- Strong experience with AWS and infrastructure fundamentals, including services such as IAM, KMS, S3, and CloudWatch, and compute or orchestration services such as EKS, ECS, or Lambda where applicable.
- Hands-on experience with infrastructure-as-code and automation-first engineering.
- Solid understanding of the software development lifecycle, CI/CD, automated testing, and safe deployment practices.
- Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and security and resiliency expectations; ability to guide peers on safe and effective usage.
- 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, and governance/catalog concepts.
- Familiarity with modern front-end technologies such as React, TypeScript, and HTML/CSS.
- Familiarity with data pipelines and working with large-scale datasets.
- Experience with distributed systems such as Apache Kafka in production environments.
- Experience with observability tooling such as Datadog, Splunk, Dynatrace, or Grafana.
Responsibilities
- Execute software solutions across design, development, and technical troubleshooting, including breaking down complex technical problems and considering approaches beyond routine solutions.
- Create secure, high-quality production code and maintain services and automation supporting AWS data-platform provisioning and pipeline enablement.
- Design, develop, and troubleshoot a Python-based control plane that automates platform provisioning, configuration, and pipeline enablement, including Databricks-on-AWS.
- Build secure, reliable backend services and APIs for self-service workflows, including 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 and monitoring, and guardrails.
- Use enterprise-authorized AI coding-assistance tools responsibly to improve code quality, delivery speed, and productivity; validate outputs through peer review, automated testing, and secure coding standards, and contribute reusable practices to the team.
- Use the software development lifecycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized through automation.
- Produce architecture and design artifacts for complex applications and ensure that software meets design constraints for security, resiliency, scalability, and cost.
- 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.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
