
at J.P. Morgan
Bulge Bracket Investment BanksPosted 12 days ago
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**Product Associate - Payments** fosters growth in a dynamic team by driving innovation in customer experiences. As an Associate in Embedded Payments, you'll support product development and collaborate cross-functionally to address customer needs. In Embedded Payments Analytics PM, you'll deliver self-service insights and enhance data discovery, usability, and trust. Key responsibilities include: user research, requirements definition, partnering with engineering teams, and managing upstream dependencies. This role requires 1+ years of experience in product management, analytics, or software delivery, with a focus on the product development lifecycle, SQL proficiency, and strong communication skills. Familiarity with Databricks, semantic layers, and Python is preferred. Join us to make a tangible impact on products and customers.
- Compensation
- Not specified USD
- City
- Not specified
- Country
- United States
Currency: $ (USD)
Full Job Description
Location: Jersey City, NJ, United States
As a Product Associate in Embedded Payments, you play a crucial role in supporting the product development process and contributing to the discovery of innovative solutions that address customer needs. Working closely with the product management team and cross-functional partners, you contribute your skills and insights to help ensure the success of our product offerings. As a valued member of the team, you have the opportunity to learn and grow in a dynamic and fast-paced environment, making a tangible impact on our products and customer experiences.
- Supports delivery of analytics products that improve discovery, usability, and trust of data for analysts, data scientists, and business users (e.g., semantic layers, reusable metrics, curated datasets, and analytics tooling).
- Assists with discovery and user research by gathering stakeholder input, contributing to journey maps, and synthesizing insights into clear problem statements and opportunities.
- Helps define requirements by writing epics and user stories (with acceptance criteria) that describe analytics workflows, metric definitions, data logic, and reporting needs.
- Partners with engineering and data teams to translate business questions into implementable analytics solutions, including KPI/metric definitions, dimensional modeling needs, and performance considerations.
- Considers upstream dependencies (ETL/ELT schedules, data quality checks, schema changes, lineage, and access controls) and helps manage impacts to downstream dashboards, models, and AI consumers.
- Supports the development of our product strategy and roadmap
- Collects and analyzes metrics on product performance to inform decision-making
- Contributes to solution discovery through collaboration with cross-functional teams to identify potential solutions that address user needs and align with business goals
- Participates in product planning sessions, contributes ideas and insights, and assists in the execution of product initiatives, ensuring timely and successful product launches
- Collaborates with the product manager to engage stakeholders and define user workflows, requirements, stories, and customer value
- 1+ years of experience (or equivalent internship/project experience) in product management, analytics, BI, data engineering, or software delivery.
- Foundational knowledge of the product development lifecycle and comfort operating in Agile teams.
- Working proficiency in SQL and ability to validate metric logic, perform exploratory analysis, and troubleshoot data issues.
- Familiarity with analytics concepts: KPIs/metrics, reporting, basic dimensional modeling, and the importance of data quality and definitions.
- Strong written and verbal communication skills, including the ability to turn ambiguous requests into clear requirements.
- Developing knowledge level of the product development life cycle
- Exposure to product life cycle activities including discovery and requirements definition
- Emerging knowledge of data analytics and data literacy
- Experience with Databricks (notebooks, SQL warehouses, jobs/workflows, Delta tables) or similar lakehouse analytics platforms.
- Familiarity with semantic/metrics layers and governed self-service analytics patterns (e.g., metric definitions, data catalogs, reusable models).
- Basic proficiency in Python (or similar) for analytics or automation.
- Experience with Jira/Confluence for backlog management and documentation.
- Exposure to AI/ML or GenAI-enabled analytics and awareness of responsible AI considerations.




