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Essential Responsibilities:
Expected Qualifications:
Additional Responsibilities & Preferred Qualifications
To successfully execute on cross-org initiatives that elevate data engineering, we need a T27 who can innovate, drive technical strategy, and ensure that our systems are scalable, efficient, and aligned with GCSC Risk’s broader goals.
As the Senior Staff Engineer, Global Risk Data Management within the Credit Infrastructure and Data organization, you will implement common data practices across functional areas that drives standardization, quality, ease of access, accelerates governance practices by treating data as a product and facilitates a data-driven culture across PayPal where data is considered a strategic asset and treated accordingly-with the proper controls and quality.
Position Responsibilities:
Must be a self-starter, work independently or as a team member. Proactively remove obstacles to ensure timely delivery of product and goals
Write clean and solid code that scales overPB of dataand enforce engineering excellence in the organization
Improve data management efficiency through AI capabilities, better process and best practices
Embed Privacy-by-Design principles into all data solutions and ensure compliance with regulatory requirements.
Provide expertise across the data product development lifecycle—spanning data engineering, architecture, and analytics—to design and deliver reusable, accessible, and high-quality data solutions.
Design data structures and taxonomies that support standardization, integration, and alignment with business processes.
Deliver technical leadership through analytical thinking, innovation, and detailed specifications.
Drive data product execution and adoption through a metrics-based approach
Define and communicate data strategy for credit products, aligned with PayPal’s technical direction, evolving credit strategies, and the external data ecosystem
Strong product sense to identify data challenges and opportunities, and assess the impact of data-driven solutions
Leverage enterprise frameworks, governance tools, and reusable architecture patterns for Credit Risk and cross organizations
Foster influential cross-functional relationships through collaboration, proactive planning, and decisive leadership to design scalable solutions across platforms and products.
Technical Leadership Qualities:
Strong collaboration skills and ability to influence across all organizations and levels within the company
Ability to communicate clearly and succinctly to all levels within the organization- translating the organizations goals into execution plans & metrics
Possess the ability to connect, engage and lead with empathy
Motivate others through a shared vision and confidence that empowers employees and teams to perform at their best
Demonstrate ability to delegate work
Operate with transparency and honesty in all interactions
Qualifications
12+ years of experience in enterprise data management, with deep expertise across disciplines including data governance, data architecture, and more.
Experience with data management and governance tools and technologies
Experience with enabling data products within large organizations
Proven experience independently developing and implementing recommended approaches to complex/ambiguous problems while making thoughtful trade-offs
Deep understanding and hands-on experience in data platforms
Strong technical acumen, being able to quickly understand technical details of a product or platform
Strong execution skills-ability to deliver results
Excellent verbal and written communication and collaboration skills to effectively communicate across diverse groups within PayPal
Comfortable working in a fast-paced, results-oriented environment
Shows initiative and exhibits a “can-do” attitude
Demonstrated willingness to be flexible and adaptable to changing priorities with the ability to motivate in a cross-functional organization
Able to see the “big picture”-the end-to-end connection points associated with data and systems
Exhibit a passion for Data Management and Analytics
Strong business acumen and interest to deeply understand PayPal’s credit use cases, data producers and data consumers’ needs/pain points
Bonus Qualifications
Familiarity with AI/ML pipelines or AI-enhanced data systems (data discovery, data quality monitoring, or integrating ML into ETL flows)
Passion for bridging the gap between data engineering and machine learning engineering.
Experience enabling ML/AI workloads, such as building feature stores, training data pipelines, or model monitoring infrastructure.
Experience in financial services, with a strong preference for exposure to the credit risk domain.
Education:
BS or advanced degree in Engineering, Computer Science, or related technical field.
Our Benefits:
Any general requests for consideration of your skills, please
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