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
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Strong experience in Data Engineering, Python, SQL, AWS Tech Stack
Exposure to knowledge graphs, entity resolution, Anomaly detection, or recommendation systems.
Strong hands-on Python development experience, with the ability to design, build, and support production-grade applications and data pipelines.
Demonstrated proficiency in PySpark and AWS Glue, with the ability to build and optimize distributed data-processing workflows.
Strong SQL skills and experience working with data processing and data engineering workloads.
Hands-on experience with AWS cloud services and building cloud-native solutions.
Solid software engineering fundamentals, with experience delivering and supporting solutions in a production 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
Preferred qualifications, capabilities, and skills
Proficiency in automation and continuous delivery methods; strong SDLC discipline (CI/CD, testing, code quality, release controls)
Advanced understanding of agile delivery, application resiliency, and secure engineering practices
Strong hands-on Java development experience
Knowledge of responsible AI practices, including secure data handling, traceability, and governance in regulated environments.
Prior experience in financial services domain
