Manager, Software Engineering
AI in this role
Lead the data infrastructure engineering team to design and scale core data platforms and distributed systems.
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
Manager, Software EngineeringTeam LeadPosition Overview
We are seeking an experienced and highly motivated Team Lead to lead our Data Infrastructure team. This role is responsible for managing a team of engineers while driving the design, scalability, reliability, and operational excellence of the company’s core data platform.
The ideal candidate combines strong technical expertise in distributed systems and data technologies with proven leadership capabilities, strategic thinking, and the ability to lead complex initiatives end-to-end — from initial design and planning through implementation, deployment, and production ownership.
Key Responsibilities
• Lead, mentor, and develop a team of Data Infrastructure engineers, fostering a culture of ownership, collaboration, and continuous improvement
• Define and execute the technical roadmap for the organization’s data infrastructure platform
• Oversee the architecture, scalability, reliability, and performance of large-scale data systems and pipelines
* Define and drive data quality standards, monitoring, and validation processes to ensure trust and reliability across the data platform
Partner with engineering and product teams to enable scalable data foundations for analytics, machine learning, and AI-driven initiatives•
• Lead cross-functional technical initiatives end-to-end, including requirements gathering, architecture design, implementation, deployment, and post-production ownership
• Partner closely with cross-functional teams including Data Engineering, Analytics, Backend Engineering, DevOps, and Product Management
• Drive operational excellence through monitoring, observability, automation, and incident management best practices
• Ensure high availability and stability of critical production systems
• Lead technical design discussions, architecture reviews, and engineering planning processes
• Establish and promote engineering standards, development best practices, and quality assurance processes
• Manage project prioritization, resource allocation, and timely delivery of strategic initiatives
• Support recruitment, onboarding, performance management, and career development of team members
• Evaluate emerging technologies and recommend improvements to the data platform ecosystem
Required Qualifications
• Bachelor’s degree in Computer Science, Engineering, or a related field
• 7+ years of experience in software engineering, data engineering, or infrastructure engineering
• 2+ years of experience managing or leading engineering teams
• Strong experience with distributed systems and large-scale data platforms
• Hands-on experience with modern data technologies and frameworks such as Kubernetes, Kafka, Spark, Flink, Airflow, DBT, Snowflake, Cassandra/ScyllaDB, or similar technologies
• Strong programming skills in Java, Scala, or Python
• Experience working in cloud-based environments such as AWS, GCP, or Azure
• Strong understanding of system reliability, performance optimization, and operational best practices
• Excellent communication, stakeholder management, and collaboration skills
• Proven ability to lead complex technical projects in a fast-paced environment
Preferred Qualifications
• Exposure to UCP (Universal Commerce Protocol) or similar interoperability standards for AI agents and payment ecosystems is a bonus.
• Experience working in an eCommerce or digital commerce environment is a strong advantage.
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.
How we score this
Manager, Software Engineering at Mastercard scores 20 out of 100 on AI centrality, which makes it AI Level 1 of 4 (Little AI) on this board. The level measures how much of the work is AI, not seniority.
AI Level 1. The work itself involves no AI, or AI only appears as scenery, such as a company tagline.
- AI Level 480 to 100
- AI Level 360 to 79
- AI Level 240 to 59
- AI Level 10 to 39
Bands come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where team leadership was part of your work. What did you do?
- Tell me about a project where distributed systems was part of your work. What did you do?
- Tell me about a project where data infrastructure was part of your work. What did you do?
- Tell me about a project where scalability was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI Agents, Team Leadership, Distributed Systems, Data Infrastructure, and Scalability. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
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