Sr. Product Manager

Visa•Bellevue, WA

6d•Hybrid

About The Position

The post purchase team at Visa is looking for a hands-on and strategic Product Manager / Product Builder to lead the development of our data integration platform and own our Dispute Management product which is part of our post purchase solutions. This role sits at the intersection of product strategy, data infrastructure, and AI-driven automation, and is critical to enabling scalable, reliable integrations across a diverse ecosystem of data sources. You will own high-impact product areas focused on post-purchase workflows, dispute lifecycle management, and integrations with external systems (issuers, acquirers, and partners). You will be able to work on both customers facing solutioning and articulate complex technical solutioning into simple and effective messaging. You will define and execute the roadmap for how we ingest, normalize, and operationalize data—leveraging modern integration patterns and AI to improve speed, resilience, and intelligence. This role requires someone who can think at the system level while also diving deep into execution details.

Requirements

10+ years of product management experience, ideally in platform, data, or API-driven products
Proven ability to operate both strategically and hands-on in complex technical domains
Strong product instincts with a bias toward action and ownership
Deep understanding of data integrations, APIs, ETL/ELT pipelines, and distributed systems
Experience working with integration tools or frameworks (e.g., middleware, iPaaS, event-driven systems)
Familiarity with data formats and protocols (REST, webhooks, batch processing, streaming, etc.)
Experience or strong interest in applying AI/ML to product problems, especially in data-heavy environments
Ability to identify where AI meaningfully improves integration efficiency or quality
Highly proactive, high-agency operator who takes ownership and drives outcomes
Comfortable navigating ambiguity and building from 0→1 as well as scaling existing systems
Strong problem-solving skills with attention to detail
Excellent verbal and written communication skills
Experience working with distributed/global teams
Ability to influence without authority and align stakeholders across functions
5 or more years of relevant work experience with a Bachelors Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD
Product Management Experience in payments, fintech, or dispute management ecosystems
Understanding of Enterprise Systems and Integrations
Background working with third-party integrations at scale (PSPs, marketplaces, SaaS platforms)
Domain experience in Dispute Management, Payments & working with merchants and banks
Product thinking and strong understanding of business metrics
Robust competency in leading early-stage product development, design, and management across markets with high regulatory constraints.

Nice To Haves

Experience in payments, fintech, or dispute management ecosystems
Familiarity with tools like Kafka, Snowflake, dbt, or similar
Background working with third-party integrations at scale (PSPs, marketplaces, SaaS platforms)
Domain experience in Dispute Management, Payments & working with merchants and banks
Experience building products for enterprise leveraging AI models or AI augmented capabilities is a plus.
Experience in a product builder role with hands on Claude coding will be a huge plus.

Responsibilities

Define the vision and roadmap for data integrations across multiple internal and external data sources (APIs, PSPs, third-party platforms, etc.)
Identify opportunities to leverage AI/ML to automate integration workflows, schema mapping, anomaly detection, and data quality improvements
Balance long-term platform investments with near-term customer and business needs
Work closely with engineering to design and deliver scalable, resilient integration frameworks
Drive end-to-end product development—from discovery and requirements to launch and iteration
Define integration standards, contracts, and best practices (e.g., versioning, schema evolution, monitoring)
Partner with data science / engineering to apply AI for: Intelligent data mapping and transformation, Automated onboarding of new data sources, Error detection and self-healing pipelines
Bring a pragmatic approach to AI—focusing on real business impact, not hype
Define success metrics for integrations (latency, reliability, coverage, cost, etc.)
Use data to prioritize roadmap decisions and continuously improve system performance
Build dashboards and insights to monitor integration health and usage