Join Mastercard as a Lead Software Engineer and contribute to building a sustainable global economy. This role offers the opportunity to lead significant software engineering projects, develop innovative solutions, and drive their delivery across more than 200 countries. Mastercard is committed to fostering an inclusive culture where diverse talents thrive and advance their careers while enjoying comprehensive benefits and development opportunities.

Lead the design and implementation of advanced software solutions, ensuring high quality and faster time to market. Drive daily system development and maintenance to meet stringent service level agreements (SLAs). Collaborate with business and technical partners to identify opportunities, assess value, and manage risks. Develop strategic technical plans and architecture proposals, acting as a subject matter expert. Present technical strategies to management and stakeholders, and research emerging technologies to provide recommendations and conduct Proofs of Concept (PoCs).

Possess expertise in writing secure code in multiple languages such as C, C++, C#, Java, and JavaScript, along with a strong understanding of secure coding standards (e.g., OWASP, CWE, SEI CERT). Proficiency in building applications using open frameworks like Spring Boot, Angular, and DXP is essential. Deep knowledge of operating system internals (Windows, Linux) for writing performant and interoperable code is required. The role demands strong debugging and troubleshooting skills, including analyzing dumps and identifying coding errors. Experience in documenting and coaching teams on development practices, coding guidelines, and design patterns is crucial. Ability to conduct technical code reviews, understand technical debt, and plan for system architecture and infrastructure capacity is expected. Familiarity with customer journeys to ensure a positive end-user experience, simplify deployment, orchestrate release workflows, and automate code vulnerability scanning is necessary. Experience in defining and reporting on test runs, conducting performance tests, and integrating generative AI tools into development workflows is a key requirement.