Lead Software Engineer - Databricks/Snowflake/AWS

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As a Lead Software Engineer at JPMorgan Chase within the Corporate Technology - Consumer and Community Banking Risk Technology team, youare 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 5+ 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)