Software Engineer II
Mastercard · O'Fallon, Missouri, United States
Our Purpose
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
Software Engineer II
Overview
The Franchise and Legal Solutions (FLS) Program enables growth in Mastercard’s products, markets, services, and innovations while protecting our brand and business in an increasingly complex and competitive environment. The team supports and protects the franchise agreement by implementing the rules of the road for anyone interacting with our business.
As a Software Engineer II, you will combine strong software engineering fundamentals with modern enterprise platforms, automation, data, and AI-enabled engineering to solve complex business problems. You will work across Java/Spring Boot applications, APIs and integrations, cloud platforms, Microsoft Power Platform, and generative AI technologies.
What You'll Do
Software Development
• Design, build, test, deploy, and support software solutions for medium-sized
initiatives and defined components of larger enterprise programs.
• Develop applications, microservices, REST APIs, and system integrations
using Java/J2EE, Spring Boot, and related technologies.
• Apply data structures, algorithms, design patterns, and software
engineering principles to solve business and technical problems.
• Build automation and business solutions using Microsoft Power Platform,
including Power Apps, Power Automate, and Dataverse.
• Create data and analytics solutions using SQL, data modeling, Power BI,
and related technologies.
• Use Python and/or TypeScript for automation, application development,
data processing, and AI integration.
• Develop enterprise AI applications and workflows using LLMs, generative AI,
RAG, and agentic patterns.
• Apply AI-assisted development tools such as GitHub Copilot, Claude Code,
or comparable coding agents within engineering workflows.
• Integrate AI models with enterprise applications, APIs, tools, and data
sources using technologies and patterns such as MCP servers and tool/API
integration.
• Build AI-ready data and retrieval pipelines using technologies such as
Databricks, Spark, embeddings, vector search, and knowledge
management.
• Create and maintain technical and software development documentation.
Design & Architecture
• Partner with business and product owners to translate defined
requirements into scalable, maintainable solutions.
• Apply established software design and architecture principles, patterns, and
standards across applications, services, integrations, data flows, and
platform dependencies.
• Determine whether traditional software development, low-code, analytics,
automation, AI, or a combination is appropriate for defined problems.
• Build cloud-native solutions and integrate secure, responsible AI
capabilities into enterprise applications.
• Design AI components using RAG, vector databases, LLM APIs, MCP/tool
integration, and agentic frameworks, considering scalability, reliability,
security, and maintainability.
• Simplify technical approaches while balancing business value, complexity,
performance, maintainability, and cost.
Quality & Secure Engineering
• Develop and maintain automated unit, functional, and integration tests
using frameworks such as JUnit and Spock.
• Apply secure coding, information security, web application security, and
applicable PCI DSS principles throughout the software development
lifecycle.
• Review code to improve quality, readability, maintainability, security, and
adherence to engineering standards.
• Build and maintain CI/CD pipelines using Git/Bitbucket, Jenkins, Maven,
Artifactory, and related tools.
• Incorporate DevSecOps practices across development, testing, deployment,
and support.
Performance & Operational Excellence
• Provide production support and troubleshoot issues across applications,
services, integrations, automation, and AI-enabled solutions.
• Use logging, monitoring, and observability practices to diagnose issues and
improve operational supportability.
• Analyze and optimize application performance, scalability, reliability, and
operational readiness.
• Improve AI solution performance, latency, cost, reliability, quality, and
operational health.
• Independently take defined engineering problems through design,
development, testing, deployment, and production support.
Collaboration
• Collaborate with engineering, product, business, security, data, and platform
partners to deliver measurable business value.
• Share technical knowledge and engineering best practices across the team.
• Contribute to Scrum or SAFe Agile delivery and help resolve cross-service
dependencies and technical challenges.
• Evaluate emerging technologies and identify opportunities for automation, AI,
and innovation.
• Demonstrate the Mastercard Way cultural values described for this role:
own it, simplify it, sense of urgency, thoughtful risk-taking, unlock potential,
and be inclusive, with a relentless focus on customers.
Required Qualifications
• At least four years of software development, design, and architecture
experience with moderately complex to complex applications.
• Strong software engineering fundamentals, including software design,
architecture, data structures, algorithms, design patterns, and system
integration.
• Proficiency developing applications, REST APIs, microservices, and
enterprise integrations using Java/J2EE and Spring Boot.
• Strong knowledge of unit, functional, and integration testing, including
frameworks such as JUnit and Spock.
• Ability to apply secure coding, application security, and information security
principles throughout the software development lifecycle.
• Knowledge of code review practices that promote quality, readability,
maintainability, security, and engineering standards.
• Understanding of performance engineering, including scalability, reliability,
monitoring, and optimization.
• Proven ability to support production applications through observability,
monitoring, troubleshooting, and reliability practices.
• Familiarity with CI/CD and DevSecOps practices using tools such as
Git/Bitbucket, Jenkins, Maven, and Artifactory.
• Knowledge of cloud, container, and enterprise platform engineering.
• Demonstrated ability to independently take defined engineering problems
from solution design through development, testing, deployment, and
production support.
• Hands-on use of AI-assisted development tools such as GitHub Copilot,
Claude Code, or comparable coding agents.
• Practical knowledge of generative AI and LLM-powered application
development, including LLM APIs, RAG, AI agents, MCP servers, and
tool/API integrations.
• Proficiency with Microsoft Power Platform technologies, including Power
Apps, Power Automate, and Dataverse.
• Knowledge of SQL, data modeling, and analytics or visualization
technologies such as Power BI.
• Ability to use Python and/or TypeScript for automation, application
development, or AI integration.
Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process