• Own and evolve the GDPR Orchestrator (build & run): the platform's core engine, its safety mechanisms (preview/dry-run, audit trail), and its operational health (deploys, on-call, observability). You design the contract areas build on and keep it reliable as adoption grows. • Build the paved road (enable, teach to fish): docs, templates, and self-serve tooling so any of our 15+ areas can plug into the platform and manage its own data rules and dependencies. You coach areas through pairing and syncs; you build the capability rather than doing the work for them. • Coordinate across areas (the cross-area bridge): align legal, security, data, and the data-producing areas on requirements, surface cross-area data dependencies, and resolve design trade-offs between them. You own the coordination; each area stays accountable for its own data and rules. • Push the foundations forward: drive open platform design questions to a conclusion and grow the platform's capabilities as Alan's data, products, and footprint expand.

• As a Senior Software Engineer on Language Clients, you'll be a technical owner of ClickHouse's Java client and JDBC driver, the foundation that our own Connectors team and a long list of external frameworks rely on to talk to ClickHouse. • Your primary users are engineers: the Connectors team building ClickHouse connectors for Kafka, Flink, Spark, and others, and the broader JVM ecosystem (data ingestion tools, BI platforms, data visualizations) that consumes the driver directly. Your job is to make sure they have a fast, reliable, well-designed foundation and to treat their feedback as the signal for where the driver goes next. • In your first year you can expect to: • Own the Java client and JDBC driver end-to-end: roadmap, API design, performance, release engineering, and partner-facing support. • Drive throughput and latency work across the client and ClickHouse server • Partner day-to-day with the Connectors team as an embedded internal customer: understand what their connectors need from the driver, and ship it. • Engage directly with external partners across data ingestion, and data visualization on interoperability, regressions, and roadmap alignment. • Ship visible, measurable wins in throughput, stability, and developer experience.

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 Lead Software Engineer Lead Software Engineer Overview: Who is Mastercard? 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 realise their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all. Role Overview The CNPF Data & AI organisation is looking for a Lead Software Engineer to help build the next generation of intelligent, agentic products and platforms powering the Mastercard Virtual C-Suite. This is a hands-on technical leadership role for an experienced engineer who combines strong software engineering fundamentals with practical experience building production-ready AI systems. You will lead the design and delivery of secure, scalable, and reliable agentic applications that can reason, orchestrate tools, interact with enterprise systems, and deliver measurable business value. You will work closely with Applied AI, Data Science, Product, Security, and Platform teams to move from concept to experimentation to governed production deployment. Position Responsibilities As a Lead Software Engineer, you will:• Lead hands-on architecture, design, and implementation of agentic applications, AI-powered services, and platform capabilities from concept through production• Define engineering patterns and best practices for production AI systems, including evaluation, monitoring, guardrails, resiliency, cost control, and rollback strategies• Drive end-to-end software delivery across the SDLC, from discovery and prototyping to testing, release, and production operations• Use engineering tools to accelerate design, coding, testing, documentation, troubleshooting, and delivery while maintaining strong engineering judgment and code quality standards• Champion an AI-enabled SDLC by improving developer workflows, automation, test generation, code review quality, release confidence, and team productivity• Partner closely with Product, Applied AI, Data Science, and business stakeholders to translate ambiguous opportunities into scalable product capabilities• Provide technical leadership through architectural decisions, design reviews, code reviews, hands-on contribution, and mentoring of engineers across the team• Build highly available, secure, and maintainable cloud-native services with strong observability, performance, and operational readiness• Shape technical roadmaps, identify short- and long-term platform needs, and influence architecture choices that enable scale, reuse, and faster delivery• Collaborate across teams and business units to solve complex business and engineering problems with practical, high-impact solution• Keep senior stakeholders informed of progress, risks, trade-offs, and implementation decisions in a clear and concise manner Ideal Candidate Qualifications:• Strong software engineering experience building scalable, secure, maintainable production systems, including experience leading complex technical initiatives end to end• Hands-on experience building and shipping AI-powered products or agentic applications using LLMs, orchestration frameworks, tool-calling patterns, retrieval, and context-aware workflows• Strong understanding of agentic system design, including planning, reasoning loops, workflow orchestration, memory, grounding, evaluation, safety, and human-in-the-loop controls• Experience taking AI solutions from prototype to production with sound engineering discipline around reliability, observability, latency, cost, security, and governance• Experience with modern AI frameworks, SDKs, and tooling for building AI applications, agent workflows, and developer productivity use cases• Strong programming skills in one or more backend languages such as Java or Python, with the ability to write high-quality, well-tested, production-ready code• Experience with modern front-end frameworks such as React and/or Next.js for building intuitive product experiences would be beneficial• Experience building services in cloud-native environments using Kubernetes and managed cloud services on AWS, Azure, or GCP• Good understanding of APIs, distributed systems, event-driven architectures, data pipelines, and integration patterns across enterprise platforms• Experience with CI/CD, automated testing, DevSecOps, and engineering automation, including the ability to improve SDLC efficiency and release quality using AI tools• Practical experience using AI coding and engineering assistants to improve productivity across design, implementation, testing, debugging, documentation, and operational support• Strong background in software security, including authentication, authorisation, secrets management, encryption, threat modelling, and secure deployment practices for AI-enabled systems• Proven ability to create reusable platforms, frameworks, or internal engineering capabilities that improve developer experience and accelerate delivery across teams• Strong product mindset with the ability to translate user needs and business goals into practical, high-impact technical solutions• Excellent collaboration and communication skills, with experience influencing across engineering, product, data science, and leadership stakeholders All About You• You are a hands-on technical leader who enjoys building and shipping real products, not just prototypes• You have experience building or operating AI-enabled or agentic applications in production and understand what it takes to make them secure, reliable, and useful at scale• You combine strong software engineering fundamentals with curiosity and good judgment in applying emerging AI capabilities to real business problems• You actively use AI to enhance your own engineering productivity and help teams adopt better ways of designing, coding, testing, documenting, and operating software• You understand where AI can accelerate delivery and where human review, engineering discipline, and thoughtful controls remain essential• You care deeply about customer value, developer experience, quality, resilience, and long-term maintainability• You are comfortable working in collaborative, cross-functional, and internationally distributed teams• You raise the bar for others through mentorship, technical leadership, and a practical, delivery-focused mindset• You communicate complex technical concepts clearly and effectively to both engineering teams and senior stakeholders Corporate Security Responsibility Corporate Security Responsibility 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; - Ensure the confidentiality and integrity of the information being accessed; - Report any suspected information security violation or breach, and - Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.

• Collaborate with clients and business stakeholders to deeply understand and clarify business needs and translate them into technical solutions. • Navigate the entire product lifecycle as a full-stack engineer: from requirements gathering, scoping, and planning, through to implementation and deployment, leveraging advanced AI tools to accelerate and optimize every stage. • Break down and explain technical concepts clearly during client interactions and team discussions. • Move fluidly across the application layers, utilizing AI tools to rapidly upskill and deliver on varied engineering challenges. While our primary stack is TypeScript/Vue.js on the frontend and Java or Python on the backend, we value strong foundational engineering skills that allow you to adapt as project directions evolve. • Write clean, maintainable, and scalable code guided by strong development principles. • Conduct code reviews and actively participate in team knowledge sharing