Lead Software Engineer DatabricksSnowflakeAWS

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

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 firms 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)

• Familiarity with modern data engineering technologies
• Exposure to cloud technologies (i.e. AWS)