Payoneer

Data Product Manager

In short

Senior Data Product Manager role at Payoneer's Data Product & Analytics unit, owning foundational data products and platform capabilities used across the company. The role combines product ownership, analytical work (hands-on SQL, data exploration), and architectural collaboration with Product, R&D, and Data Engineering teams. Requires 5+ years in Data Product Management or related hands-on data roles with proven ownership of complex data or technical products, strong SQL skills, and understanding of modern data architecture and cloud data platforms.

About Payoneer
Founded in 2005, Payoneer is the global financial platform that removes friction from doing business across borders, with a mission to connect the world's underserved businesses to a rising global economy. We're a community with over 2,500 colleagues all over the world, working to serve customers and partners in over 190 countries and territories. By taking the complexity out of financial workflows – including everything from global payments and compliance to multi-currency and workforce management, to providing working capital and business intelligence – we give businesses the tools they need to work efficiently worldwide and grow with confidence.
Senior Data Product Manager — Hybrid — Full-time
You will join Payoneer's Data Product & Analytics unit, the central hub for data products and capabilities across the company, as a hands-on Senior Data Product Manager. You will own and evolve foundational data products and platform capabilities that enable teams across Payoneer to develop, manage, and consume data at scale. Combining product ownership, analytical skills, and architectural thinking, you will shape product strategy, modernize existing solutions, and introduce scalable, reusable capabilities. Working closely with Product, R&D, Data Engineering, and internal data consumers, you will help ensure our data foundations support evolving business needs and AI-driven use cases.
Responsibilities
•Own the product vision, strategy, and roadmap, collaborating across data domains to build scalable, reusable capabilities
•Partner with Product, R&D, and Engineering early in discovery and design, influencing solutions and anticipating future data needs
•Shape scalable, reusable solutions with Engineering, balancing business requirements, architecture, performance, and cost efficiency
•Apply hands-on analytical skills to explore data, investigate issues, validate business logic, and inform product decisions
•Drive self-service and user experience, simplifying data consumption and improving developer and data consumer autonomy
•Ensure AI-ready, reliable data products with clear business definitions, quality expectations, documentation, and managed dependencies
•Lead initiatives end to end, from requirements and solution evaluation (including build-vs-buy) through delivery and adoption
•Measure product success and value, tracking adoption, usability, efficiency gains, and the impact of shared capabilities
Requirements
•5+ years in Data Product Management or related hands-on data roles, with proven ownership of complex data or technical products
•Strong understanding of modern data architecture, data modeling, data pipelines, and cloud data platforms
•Strong SQL and analytical skills, with hands-on data exploration, profiling, and validation that inform product decisions
•Strong product mindset and business acumen, able to challenge requirements and turn common needs into reusable data capabilities
•Ability to engage in technical and architectural discussions and collaborate effectively with Engineering teams
•Proven experience leading cross-functional initiatives, aligning stakeholders, and driving delivery independently
•Excellent communication skills and strong English proficiency
Advantages (nice to have)
•Experience with shared data products, data platforms, or developer-facing infrastructure
•Experience with BigQuery, modern transformation or orchestration tools, data governance, metadata, or observability
•Experience supporting AI applications or modernizing legacy data platforms

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