Lead Data Platform Engineer (AWS)

JPMorgan Chase & Co.; Plano (TX); On-site; USD 100,000 - 130,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a Lead Software Engineer to join their Corporate Sector's AI/ML & Data Platforms team in Plano, Texas. This role involves designing secure Data Lake solutions and improving coding standards using AI-assisted tools.

The ideal candidate will have over 5 years of experience in software engineering, strong skills in AWS, Python, and Java, and a deep understanding of secure data handling practices.

Qualifications

• 5+ years of applied experience in software engineering.
• Hands-on experience in AWS Data Management tools like Glue, EMR, and Redshift.
• Proficient in Python, Java, and database query languages.

Responsibilities

• Designs and implements secure Data Lake solutions in AWS.
• Develops IaaS including Terraform configurations and Python/Java scripts.
• Collaborates with teams to deliver software solutions and troubleshoot technical issues.

Skills

AWS Data Management

Python

Java

Terraform

AI-assisted software development

Education

Formal training or certification in software engineering

Job description

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Corporate Sector's AI/ML & Data Platforms team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Designs and implements secure Data Lake solutions within the AWS environment.
• IaaS (Infrastructure As A Code) development including Terraform configuration script, Python scripts or Java programming to create services and resources in AWS Cloud environment.
• Explores and evaluates new Data Management features offered by AWS.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, MCP server creation, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• 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.
• Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
• Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
• Collaboration with different teams to execute software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
• Contributes to software engineering communities of practice and events that explore new and emerging technologies

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Hands-on experience in AWS, especially in Data Management arena such as Glue, EMR, Athena, Redshift, and Lake Formation.
• Proficient in Python, Java, and database query languages for applications development, debugging, and maintenance in a large corporate environment
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong 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 coaching engineers on safe, compliant adoption within delivery practices
• Overall solid knowledge of the Software Development Life Cycle

Preferred qualifications, capabilities, and skills

• Hands-on Terraform or other IaaS experience
• AWS certification on Solutions Architect Associate / Developer level
• Other advanced level certification on programming languages.
• Knowledge working with PCI and sensitive data workloads