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Tier-1 brand, metro location, mid-level generalist title and broad skillset drive high applicant competition.
Requires financial-industry audit experience and platform expertise, making background fit highly domain-sensitive.
Mandatory five-plus years, financial-industry experience, and diverse mandatory technical skills make filters highly strict.
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Own end-to-end development and maintenance of internal audit software platform including front-end, back-end data pipelines, and AI/ML integration.
Build and maintain scalable, reliable data pipelines and operationalize machine learning models to enhance audit analytics and reporting.
Ensure system reliability through CI/CD automation, monitoring, and documentation while collaborating cross-regionally with local and New York teams.
5+ years experience with cross-regional collaboration in the financial industry.
Bachelor’s or Master’s degree in System Engineering, Computer Science, Data Engineering or related field.
Proficiency in Python, SQL, and one or more modern programming languages; experience with data pipeline orchestration tools and ETL frameworks (e.g., Snowflakes).
Strong Linux, network, and cyber engineering knowledge; familiarity with cloud and local deployments and ML model deployment.
Experienced full-stack systems engineer with strong coding skills and track record in building reusable, production-grade software components for audit or analytics platforms.
Comfortable operating in a complex, global financial environment coordinating across time zones and cultures.
Pragmatic technologist focused on reliable, maintainable system architecture emphasizing operational excellence, security, and minimal manual intervention.