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We are seeking a hands-on Manager, Software Engineering to spearhead the development of scalable data platforms, backend systems, and data pipelines for Mastercard's Portfolio Intelligence products. This pivotal role initially involves leading a team of Data Engineering contractors, offering strong technical guidance in architecture, delivery, and engineering excellence. The ideal candidate is an experienced practitioner adept at building and managing modern data platforms and backend services, capable of evaluating architecture proposals and code. Leveraging AI-powered engineering tools to enhance productivity, quality, and delivery speed is key. As the team grows, this leader will be instrumental in cultivating a high-performing organization of full-time engineers.

Provide expert technical leadership for backend services, data pipelines, and platform capabilities essential for analytics, reporting, and AI-driven products. Review and approve architecture and design documents, ensuring solutions are scalable, maintainable, secure, and align with long-term platform vision.

Conduct thorough code reviews, championing best practices in reliability, security, and operational excellence. Collaborate closely with architects, product managers, and engineering leaders to define technical roadmaps and prioritize investments.

Drive the adoption of contemporary engineering practices, including automation, observability, robust testing frameworks, CI/CD integration, and infrastructure-as-code. Champion the effective utilization of AI-assisted development tools to boost developer productivity, enhance code quality, improve documentation, and accelerate delivery velocity.

Lead and coordinate a team of Data Engineering contractors, setting clear expectations, fostering accountability, and ensuring successful delivery outcomes. Manage the entire contractor lifecycle, from onboarding and work planning to quality assurance and performance oversight. Cultivate a culture rooted in ownership, collaboration, continuous improvement, and technical distinction. Play a crucial role in shaping the long-term team structure, facilitating a transition towards a blended organization of full-time and contingent engineers.

Partner effectively with Product and Program teams to translate business objectives into executable engineering plans. Ensure predictable delivery of complex projects while maintaining a balance between technical debt reduction, platform investments, and new feature development. Proactively identify, plan mitigation for, and track technical risks. Measure and drive improvements in key engineering metrics encompassing reliability, quality, operational health, and delivery effectiveness.

Minimum 8 years of software engineering experience, with recent hands-on expertise in designing and building production systems. Requires at least 2 years in an engineering leadership, technical lead, or people management capacity. Proven experience leading or managing contractor and/or distributed engineering teams is essential.

Demonstrate strong experience in building backend platforms, APIs, and data-intensive applications. Expertise in designing and implementing scalable data pipelines and data processing solutions is required. Must have experience reviewing architecture documents, technical designs, and production-quality code. Experience working within Agile product development methodologies and collaborating with globally distributed teams across different time zones is also necessary.

Essential technical skills include strong programming proficiency in Java, Python, Scala, or similar languages. Experience with distributed data processing technologies like Spark is crucial. Familiarity with modern data platforms such as Snowflake, Databricks, or Hadoop is expected. Proficiency 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 required. Experience with CI/CD pipelines, version control systems, automated testing, and DevOps practices is essential. Familiarity with major cloud platforms like AWS, Azure, or GCP is also necessary.

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