Software Engineer II

Mastercard•O'fallon, MO

6d•$92,000 - $147,000•Onsite

About The Position

Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships, and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all. You will design and build the next-generation Decision Management Platform: the real-time system that scores and approves billions of payment transactions. The role is hands-on. You will write code, run prototypes, and use AI coding tools every day to ship faster and at higher quality. You’ll be joining a high‑growth team that’s actively expanding to meet increasing scale and impact. You will work directly with engineers, architects, and product managers to deliver features that make the platform faster, more reliable, and cheaper to run.

Requirements

Several years of software engineering experience with real contributions to complex systems or shared platforms.
Hands-on experience with distributed systems running at high throughput and sub-second latency. You understand multi-region availability, consistency, backpressure, and capacity.
Solid grasp of modern software engineering practices, cloud-native architectures, and AI/data platforms.
Clear communicator who works well across teams.
Bachelor's degree in Computer Science, Software Engineering, or a related field — or equivalent experience.

Nice To Haves

You ship. A track record of delivering real features in real distributed systems at scale.
You build for scale. Hands-on experience with high-throughput, low-latency systems — ideally streaming or real-time decisioning.
You thrive in startup-mode teams. Experience at a startup or in a startup-like environment inside a larger company: small teams, shifting priorities, owning things end-to-end.
You're polyglot and curious. Comfortable working in several languages and eager to pick up new ones, frameworks, and tools as the problem demands.
You use AI tools well. Claude Code, Copilot, or similar are part of how you work, not something you've tried once.
You write clear code and clear words. You can explain a design to an engineer and a product manager.
You care about the craft. Tests, observability, and clean interfaces are not optional for you.
You collaborate. You work well with engineers, data scientists, and product partners.
Decisioning Data & Features: Data platforms for decisioning: lakehouses, delta lakes, distributed logs. Feature platforms: defining, validating, and serving features for batch and real-time use. Data models for events, features, reference data, labels, and outcomes. Data contracts, lineage, and quality checks.
High-Throughput, Low-Latency Systems: Event streaming and high-volume pipelines. Distributed caches and in-memory data grids. Sub-second transaction processing. Rules engines.
AI & ML Systems: Training, deploying, refreshing models, and serving low-latency inference. LLM integration, prompt engineering, and agentic patterns. Model monitoring: drift, feedback loops, production reliability.
Decisioning Tooling: Authoring, testing, and deploying business rules. Tools that let authors validate rules and models before they ship. Operator workflows: approvals, observability, and explaining live decisions.
Cloud & DevOps: AWS and cloud-native patterns. CI/CD, automation, observability, GitOps.
Advanced degrees are a plus, not a requirement.

Responsibilities

Write production code for services, tooling, and platform features.
Design and implement components of large distributed systems.
Build reusable services, libraries, and integrations.
Take prototypes from idea to working software.
Pick the right frameworks, libraries, and tools by weighing quality, cost, latency, and reliability.
Make clear trade-offs in the systems you own and explain them to your team.
Use AI coding tools as your default way of working.
Share patterns, demos, and tips with your team so they get the same leverage.
Automate the boring parts of development.
Make the customer experience better across the services you work on.
Simplify designs to cut cost or latency without losing capability.
Pay down technical debt, fix resiliency gaps, and close operational risks.
Contribute to designs that span multiple services.
Give useful feedback in design and code reviews.
Mentor peers and junior engineers.
Interview candidates and help raise the hiring bar.

Benefits

insurance (including medical, prescription drug, dental, vision, disability, life insurance)
flexible spending account and health savings account
paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave)
80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro-rated based on date of hire
10 annual paid U.S. observed holidays
401k with a best-in-class company match
deferred compensation for eligible roles
fitness reimbursement or on-site fitness facilities
eligibility for tuition reimbursement
56 hours of Paid Sick and Safe Time
jury duty leave
on-site fitness facilities in some locations