Lead Software Engineer

Lead Software Engineer Join us in unlocking infinite opportunity in Mastercard s Commercial Solutions organization.
In Commercial Solutions, we empower businesses with innovative, trusted payment solutions that deliver seamless, digital-first experiences globally. We are transforming the industry by building world-class travel and B2B platforms for corporate clients.
As a key leader in our digital transformation journey, you will shape next-generation financial platforms driving architecture, scalability, and engineering excellence to deliver secure, cloud, enterprise-scale solutions.

Overview
Mastercard is seeking a Lead Software Engineer with strong expertise in system design, distributed systems, and platform architecture. You will lead the design and delivery of scalable, resilient applications powering transaction processing, analytics, APIs, and modern user experiences.
This role combines technical, architecture ownership, and hands-on execution, with a focus on mentoring teams and driving engineering standards across the stack.

Role & Responsibilities
Architecture & Platform Engineering
Lead architecture and system design for large-scale distributed cloud platforms with focus on scalability, resiliency, and security
Design and evolve microservices, APIs, and event-driven architectures
Translate business requirements into robust platform solutions and technical strategy
Own non-functional requirements including performance, reliability, fault tolerance, and observability
Drive adoption of cloud-native patterns, 12-factor principles, and domain-driven design
Full-Stack & Engineering Excellence
Build and evolve high-performance full-stack applications using Java, Angular, and cloud platforms (PCF/Kubernetes)
Establish and enforce engineering standards for code quality, security, and performance
Lead design reviews, code reviews, and architecture governance
Drive CI/CD, DevSecOps, and automation practices
UI / Front-End Leadership
Lead development of modern Angular-based architectures using component-driven and micro-frontend patterns
Define and Build reusable UI components and scalable design systems
Optimize applications for performance, latency, and cross-platform compatibility
AI & Intelligent Systems
Architect and integrate AI-powered capabilities (e.g., recommendations, anomaly detection, predictive insights)
Drive adoption of AI-assisted development tools to enhance productivity
Ensure responsible AI practices including governance, explainability, and compliance
Collaboration
Provide technical mentorship, elevating engineering capabilities across teams
Influence technology strategy, frameworks, and platform evolution
Collaborate with product, architecture, and BizOps teams to align on roadmap and delivery
Ensure compliance with Mastercard engineering, security, and enterprise standards

All About You
Technical Expertise
Strong foundation in computer science fundamentals, algorithms, and system design
Proven experience building large-scale distributed systems and microservices architectures
Deep expertise in Java (Spring Boot) and backend engineering
Strong understanding of API design, event-driven systems, and messaging frameworks
Experience with cloud platforms (PCF, Kubernetes)
Solid knowledge of relational databases (Oracle) and data modeling at scale
Expertise in performance engineering, resiliency, and scalability patterns
Hands-on experience with CI/CD, Git, automated testing, and release pipelines
Experience with observability, monitoring, and production support
UI / Angular Expertise
Deep expertise in Angular, TypeScript, and state management (NgRx)
Experience with micro-frontends and component-driven architectures
Strong understanding of performance optimization, lazy loading, and rendering strategies
Familiarity with UI testing frameworks (Cypress)
Strong focus on usability, accessibility, and intuitive design
AI & Modern Engineering
Experience integrating AI/ML models via APIs or enterprise workflows
Familiarity with GenAI/LLMs and AI-driven product capabilities
Understanding of data pipelines and model deployment patterns
Ability to apply AI to enhance user experience and operational efficiency