Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Closely work with external vendors, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
Adds to team culture of diversity, opportunity, inclusion, and respect
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
Required qualifications, capabilities, and skills
Bachelor’s Degree in Computer Science, Cybersecurity, Data Science, or related disciplines
Formal training or certification on engineering concepts and 5+ years of experience in system engineering or software development.
Hands-on practical experience delivering system design, application development, testing, and operational stability.
Proficient in coding using the following technology stack: Java, Junit, Maven, Hibernate, Spring Boot, Spring JPA, Spring Batch
Hands-on experience with Databricks and/or Apache Spark for batch data processing — Delta Lake, Spark SQL, PySpark or Spark with Java/Scala, and job orchestration
Strong programming experience in Python for data engineering, automation, and scripting
Familiarity with one or more DBMS like Oracle, MySQL, or others
Proficient in all aspects of the Software Development Life Cycle
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
Advanced understanding of agile methodologies such as CI/CD, Applicant Resiliency, and Security and proficient in automation and continuous delivery methods
Preferred qualifications, capabilities, and skills
Previous working experience with Payment technology, and/or Reg Reporting tech will be a plus
In-depth knowledge of the financial services industry (payment products preferred) and related regulatory landscape and their IT systems
Experience applying AI/ML models or large language models (LLMs) to data pipelines, reconciliation, or anomaly detection
Exposure to cloud technologies, Splunk, Apache Kafka, Grafana
