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Executive Director, Data Platform Product Lead - Merchant Services
Job in
Wilmington, New Castle County, Delaware, 19801, USA
Listed on 2026-06-02
Listing for:
JPMorgan Chase & Co.
Full Time
position Listed on 2026-06-02
Job specializations:
-
IT/Tech
Data Engineer, Data Science Manager, Data Analyst
Job Description & How to Apply Below
Category:
Product Management
Job Schedule:
Full time
Posted Date: T17:52:35+00:00
Job Shift: Day
Base Pay/Salary:
Palo Alto,CA $-$;
Jersey City,NJ $-$;
Brooklyn,NY $-$
Ignite your passion for product innovation by leading customer-centric development, inspiring solutions, and shaping the future with your strategic vision and influence.
As a Product Director in Data Platform, you lead innovation through the development of products and features that delight customers. As a leader on the team, you leverage your advanced capabilities to challenge traditional approaches, remove barriers to success, and foster a culture of continuous innovation that helps inspire cross-functional teams create groundbreaking solutions that address customer needs.
As the Data Platform Product Lead, you will drive the vision, strategy, and execution for a scalable, secure, and high-performance data platform. You'll enable internal and external clients to access, analyze, and operationalize data through robust APIs, self-service tools, and managed services. Your leadership will ensure the platform is engineered for extensibility, reliability, and compliance, supporting diverse analytics and reporting needs across the payments ecosystem.
Job responsibilities
* Lead the design and delivery of a cloud-native, modular data platform (e.g., leveraging Snowflake, AWS/Azure, SQL, Python, Tableau, Alteryx) that supports multi-tenant, self-service analytics and data productization.
* Define and implement platform APIs, data pipelines, and integration patterns to enable seamless onboarding, ingestion, and consumption of merchant, network, and issuer data.
* Champion automation, scalability, and resilience in platform operations, including CI/CD, monitoring, and incident response.
* Develop and commercialize platform capabilities as managed services, including "insights as a service," reporting, and data enrichment.
* Establish reusable data assets, visualization standards, and self-service tools for internal product teams and merchant clients.
* Drive adoption through clear service packaging, pricing, and value realization frameworks.
* Set and enforce standards for data quality, metadata, lineage, and model lifecycle management.
* Govern source onboarding, integrations, and pipelines to ensure secure, compliant, and analytics-ready data delivery.
* Embed robust risk management, privacy, and regulatory controls, with audit-ready documentation.
* Partner with engineering, data science, and architecture teams to deliver platform features and enhancements.
* Build and mentor a high-performing team of product managers, data engineers, and analytics leaders.
Required qualifications, capabilities, and skills
* 10+ years of experience in data platform engineering, product management, or technology leadership, preferably in payments, merchant acquiring, or financial services.
* Deep technical expertise in cloud data platforms, distributed systems, and analytics tool chains (Snowflake, AWS/Azure, SQL, Python, Tableau, Alteryx).
* Proven experience designing and delivering platform-as-a-service solutions, including API management, self-service enablement, and managed data products.
* Strong understanding of data governance, security, and compliance in regulated environments.
* Track record of leading cross-functional teams, driving platform adoption, and commercializing data services.
* Engage with internal and external stakeholders to align platform strategy, communicate technical roadmaps, and drive adoption
Preferred qualifications, capabilities, and skills
* Recognized thought leader in data platform or PaaS domains.
* Experience with platform automation, CI/CD, and Dev Ops practices.
* Experience with AI Acceleration to build market ready platforms.
* Expertise in data quality, metadata, lineage, and model deployment best practices.
* Familiarity with experimentation, A/B testing, and measurement frameworks.
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