Lead Technical Program Manager

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About the role

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 Technical Program Manager An AI‑First Technical Program Manager who organizes and accelerates delivery across agent‑amplified pods, using the written specification as the single source of truth and DORA/Core4 metrics as the primary delivery signal. Operates as a connective role in a shared pool — reaching into every pod to keep the program's coordination, risk, and dependency view current without orchestrating day‑to‑day — while preserving every SOX/PCI control gate. Manifest definitions, release integration, scope, and dependencies are tracked with data so all outputs are production‑ready at launch. Responsibilities Organizes and accelerates related technology projects across multiple pods, aligning dependent organizations; identifies and removes blockers and always finds the path forward. Charts the pod‑to‑pod relay (which pod builds each Epic stage), aggregates cross‑Big‑Rock risks and dependencies into a single view, and drives execution through the monthly Epic cycle anchored to the user guides. Serves as the source of truth for status; generates the Monthly Technical Review (MTR), RAID Log, and DORA rollup directly from the session record using M365 Copilot, so the ledger stays current without hand‑keeping. Measurably improves, streamlines, and eliminates excess process and ceremony — naming the reader of every artifact before it is kept and retiring legacy ceremonies deliberately. Keeps abreast of the evolving AI‑engineering landscape; understands the practical read of what AI can and cannot do — that AI amplifies engineering judgment and leaves that judgment with the human — and asks the right questions to evaluate scalability, latency, security, performance, and data integrity. Works with ambiguity and limited guidance, comfortable with the indeterminism of agent‑driven delivery, kept honest by the monthly user guides and real‑customer feedback. Transforms raw thoughts into clear, concise communication and can communicate with confidence 3+ levels up; fluent in prompt patterns and agent‑session discipline to produce review‑ready output on the first pass. Understands the needs of engineers and BizOps/SRE teams, including the verification tax and the build‑and‑review, follow‑the‑sun pairing that manages it. Reviews metrics and proactively seeks improved data and mechanisms for visibility, keeping the program aligned to objectives while preserving all control gates — AIFE removes coordination overhead, not controls. Plays an active role in mentoring, recruiting, and growing agent fluency and the Expert‑Generalist skill ladder across pods. Experiences Anticipate bottlenecks and factor in contingency for unplanned delays, including transition risk from Scrum to the pod model. Define success metrics where they are missing, defaulting to the four DORA/Core4 metrics: Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Failed Deployment Recovery Time. Recognize discordant views and resolve them through constructive dialog; act as the program's anti‑confirmation‑bias check. Continuously assess development processes, test plans, and operations to improve concurrent delivery, including tests‑first/spec‑driven parallelism and same‑session documentation currency. Streamline or eliminate ex

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