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 JPMorgan Chase within the Corporate Technology - Consumer and Community Banking Risk Technology 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
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Develops secure high-quality production code using the syntax of at least one programming language with limited guidance in maintaining efficient algorithms that integrate seamlessly with relevant systems
- 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, 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
- Implements and manages data solutions using Snowflake, including data modeling, performance tuning, and secure data sharing
- Develops workflows and ETL pipelines using Python, Databricks and Spark to optimize data processing and transformation at scale
- Frequently utilizes SQL with understanding the role of NoSQL databases in the marketplace, and applies Spark for distributed data processing and analytics
- Gathers, analyzes, and synthesizes large diverse data sets to develop visualizations and reporting that drives continuous improvement of software applications and systems
- Applies knowledge of tools within the Software Development Life Cycle toolchain to improve the value realized by automation
- Gathers, analyzes, and draws conclusions from large, diverse data sets to identify problems and contribute to decision-making in service of secure, stable application development
- Adds to team culture of diversity, opportunity, inclusion and respect, as a lead on the team - driving projects independently and providing technical and architectural guidance with junior engineers
Required qualifications, capabilities, and skills
- Formal training or certification in software / data engineering concepts and 8+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, operational stability and statistical data analysis, including selecting appropriate tools and identifying data patterns
- Advanced in one or more programming language(s) and framework(s) (i.e., Python 3, ETL, Spark, Snowflake, Databricks, SQL, NoSQL, Terraform-based infrastructure deployments, etc.)
- Significant experience with data migration and platform migration for data projects, including planning, execution, and post-migration support
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, Security, and proficient in all aspects of the Software Development Life Cycle
Demonstrate 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
- Demonstrated experience in API-driven development, particularly using fast API on AWS ECS with API Gateway integration, and running APIs from AWS Lambda
- Proficient with deployment pipelines such as Git, Julies, Jenkins, and Spinnaker along with strong skills in building test scripts, and using True CD for coing and testing
- Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
- Practical cloud native experience (i.e., active knowledge of AWS functions - ECS, Lambda, API Gateway, and other general services)
Preferred qualifications, capabilities, and skills
- Familiarity with modern data engineering technologies
- Exposure to cloud technologies (i.e., AWS)