Principal software engineer data / analytics

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. Principal Software Engineer 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. As a Principal Engineer within the Decision Stream program, you will combine enterprise-scale technical leadership with hands-on engineering for the next-generation Decision Management Platform. You will actively design, code, prototype, and validate core platform capabilities, using modern AI-assisted development tools as part of day-to-day software engineering to move faster, improve quality, and help teams adopt better ways of building. Key areas of focus include leveraging disruptive technologies in real-time AI inferencing and decisioning to improve product effectiveness, increase business delivery, strengthen technical resilience, and lower cost of ownership. You will work closely with technology executives, senior leaders, and engineers to shape the overall AI & DPE technology strategy. Platform & Product Development Build software, tooling, and platform capabilities. Evaluate systems, frameworks, and tools across quality, cost, latency, scalability, reliability, and maintainability. Apply AI tools as part of daily engineering practice to real product and platform problems. Teach and model the adoption of AI-assisted development, modern languages, and current engineering practices. Improve developer experience through automation, AI-assisted workflows, and platform thinking. Customer Experience & Platform Strategy Own and improve end-to-end customer experience across a portfolio of services and applications. Simplify and optimize architecture strategies to balance cost, performance, and business value. Drive organization-wide initiatives to advance software engineering craftsmanship and best pratices. Represent the organization through public speaking, technical blogs, and white papers on emerging technologies. Participate in Principle-level architecture reviews and resolve enterprise-wide technical and regulatory challenges. Conduct technical interviews to raise the performance bar and attract top talent. Champion AI-assisted engineering as a default working practice and align it with organizational values. You are an awesome engineer and leader who is passionate about joining a like-minded team at Mastercard AI & Decision Product Enablement. You thrive on solving complex engineering challenges at scale and have experience building high-speed streaming platforms or distributed systems at hyperscaler-level performance. AI-native engineer — uses AI-assisted development as default Streaming-first mindset — experience with low-latency pipelines Collaborative — works across engineering and data science teams Decisioning Data & Feature Platforms Data architectures for decisioning: lakehouses, delta lakes, distributed logs, and product-aligned data models. Feature catalogs and engineering platforms for reusable, governed features that can be defined, discovered, validated, and served across batch and real-time decisioning. Decisioning data models covering events, derived features, reference data, labels, outcomes, and policy data consumed by rules, models, and agentic workflows. Data contracts, lineage, freshness, and quality controls that keep decisioning data trustworthy, explainable, and production ready. Event streaming and high-throughput data pipelines. Low-latency data technologies -distributed caches, in-memory data grids. AI & ML Systems ML lifecycle engineering: model training, deployment, refresh, and low-latency inference. Agentic AI patterns, LLM integration, and prompt engineering applied to platform and product problems. Model observability, drift detection, and feedback loops that keep AI systems reliable in production. Decisioning Tooling & UX Authoring, testing, and deployment of business rules engine rules and similar decisioning logic. Tooling that helps authors validate rules, models, and policies pre-deployment. Cloud Infrastructure, Platform Engineering & Dev Ops AWS infrastructure engineering and cloud-native platform patterns. Dev Ops and platform engineering -automation, CI/CD, observability, Git Ops. Extensive experience in software engineering and technical leadership Expertise in cloud, AI/data platforms, and modern engineering practices Bachelor’s degree (or equivalent); Telecommuting and/or working from home may be permissible pursuant to company policies. 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. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact View email address on click.appcast.io. Do not include any medical or health information in this email. All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: Abide by Mastercard’s security policies and practices; Report any suspected information security violation or breach, and In line with Mastercard’s total compensation philosophy and assuming that the job will be performed in the US, the successful candidate will be offered a competitive base salary and may be eligible for an annual bonus or commissions depending on the role. Mastercard benefits for full time (and certain part time) employees generally include: insurance (including medical, prescription drug, dental, vision, disability, life insurance); flexible spending account and health savings account; 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro-rated based on date of hire; S. observed holidays; fitness reimbursement or on-site fitness facilities; eligibility for tuition reimbursement; and on-site fitness facilities in some locations.