Mastercard is a global leader in technology and payments, committed to building a sustainable economy for everyone. We operate in over 200 countries, providing secure, simple, and accessible digital payment solutions. Our innovative technology, strong partnerships, and extensive networks empower individuals, businesses, and governments to reach their full potential.

We are seeking a dynamic and hands-on Manager for Software Engineering to lead the development of critical data platforms, robust backend systems, and efficient data pipelines. This role is instrumental in supporting Mastercard's Portfolio Intelligence products. Initially, the focus will be on guiding a team of Data Engineering contractors, offering strong technical expertise in architecture, delivery, and engineering best practices. The ideal candidate is a seasoned professional with proven experience in building and managing modern data platforms and backend services. You will be adept at reviewing architecture proposals and code, leveraging AI tools to enhance productivity, quality, and delivery speed. As the team evolves, you will be pivotal in shaping and developing a high-performing organization of full-time engineers.

Provide comprehensive technical leadership for backend services, data pipelines, and platform capabilities that underpin analytics, reporting, and AI-driven products. Critically review architecture and design documents to ensure scalable, maintainable, and secure solutions aligned with long-term platform strategy. Oversee code reviews, championing engineering excellence, reliability, security, and operational best practices. Collaborate with architects, product managers, and engineering leaders to define technical roadmaps and prioritize investments. Drive the adoption of modern engineering methodologies, including automation, observability, robust testing, CI/CD, and infrastructure-as-code. Champion the effective utilization of AI-assisted development tools to boost developer productivity, code quality, documentation, and overall delivery velocity.

Lead and coordinate a team of Data Engineering contractors, establishing clear performance expectations, accountability, and delivery objectives. Manage the end-to-end contractor lifecycle, including onboarding, work planning, quality assurance, and performance oversight. Cultivate a team culture that values ownership, collaboration, continuous improvement, and technical mastery. Play a key role in designing the long-term team structure and facilitating a transition towards a hybrid organization of full-time and contingent engineers.

Partner effectively with Product and Program teams to translate business objectives into concrete engineering plans. Ensure the predictable delivery of complex initiatives while expertly balancing technical debt, strategic platform investments, and essential feature development. Proactively drive technical risk identification, develop mitigation strategies, and meticulously track execution progress. Measure and continuously improve key engineering metrics focused on reliability, quality, operational health, and delivery effectiveness.

We require a minimum of 8 years of software engineering experience, including recent hands-on experience in designing and building production systems. A minimum of 2 years in engineering leadership, technical lead, or people management roles is essential. Prior experience leading or managing contractor and/or distributed engineering teams is necessary. You should possess strong expertise in building backend platforms, APIs, and data-intensive applications. Experience designing and implementing scalable data pipelines and data processing solutions is crucial. Proficiency in reviewing architecture documents, technical designs, and production-quality code is expected. Experience working within Agile product development environments and collaborating effectively with teams across different time zones is also required.

Key technical proficiencies include strong programming skills in Java, Python, Scala, or similar languages. Experience with distributed data processing technologies like Spark is essential. Familiarity with modern data platforms such as Snowflake, Databricks, or Hadoop is required. Expertise in designing RESTful APIs, microservices, and cloud-native applications is a must. A solid understanding of data modeling, database design, and data architecture principles is necessary. Experience with CI/CD pipelines, version control, automated testing, and DevOps practices is expected. Familiarity with cloud platforms like AWS, Azure, or GCP is also a requirement.

Preferred qualifications include experience building platforms that support advanced analytics, machine learning, or AI use cases. Experience in defining engineering standards and governance practices is beneficial. Experience modernizing legacy data platforms and migrating workloads to cloud environments is a plus. Additionally, experience implementing AI-assisted software development workflows using tools such as GitHub Copilot, Claude, ChatGPT, or similar technologies would be advantageous.