• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
• Develops secure and high-quality production code, and reviews and debugs code written by others
• 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.
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
• Generates data models for their team using firmwide tooling, linear algebra, statistics, and geometrical algorithms
• Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way
• Evaluates and reports on access control processes to determine effectiveness of data asset security with minimal supervision
• Create reports in JIRA, Confluence, and other platforms as needed, to improve product line reporting workflow, JIRA and resource analysis, Sprint results and efficiency, other metrics

Required qualifications, capabilities, and skills

•  Formal training or certification on software engineering concepts and 5+ years applied experience
• 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
• Strong Python programming skills, including unit and integration testing
• Strong SQL skills and experience with SQL-based transformation tooling .
• Experience designing and operating orchestration pipelines using Airflow or similar tools
• Experience designing and building streaming pipelines using Kafka, Pub/Sub, or similar messaging systems
• Experience and proficiency across the data lifecycle
• Demonstrated experience delivering in an agile, fast-paced engineering environment
• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security