Sr Staff Data Engineer

PayPal • San Jose, CA

1d • Hybrid

About The Position

PayPal has been revolutionizing commerce globally for more than 25 years, creating innovative experiences that make moving money, selling, and shopping simple, personalized, and secure. They operate a global, two-sided network connecting hundreds of millions of merchants and consumers, offering proprietary payment solutions and flexibility in funding sources. PayPal, Venmo, and Xoom products facilitate safe fund transfers, and they provide merchants with end-to-end payment solutions, including authorization, settlement, instant fund access, customer connection, exchange/return processing, and risk management, while enabling cross-border trade. The company is guided by core values of Inclusion, Innovation, Collaboration, and Wellness. This role, Senior Staff Engineer, Global Risk Data Management within the Credit Infrastructure and Data organization, requires a strong product sense to directly support the business. It focuses on scaling data management capabilities and driving high-impact, cross-functional initiatives that shape the future of data at GCSC Risk. The position demands innovation, technical strategy, and ensuring data solutions are scalable, efficient, and aligned with GCSC Risk’s broader goals. The individual will implement common data practices across functional areas to drive standardization, quality, ease of access, accelerate governance practices by treating data as a product, and foster a data-driven culture where data is considered a strategic asset. This role will help rethink how data is used to make credit decisions at PayPal, driving the creation of delightful, frictionless, and compliant payment experiences while maximizing the value of data, and supporting the implementation and adoption of the Enterprise Data Management & Governance (EDG) program.

Requirements

• 8+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.
• 12+ years of experience in enterprise data management, with deep expertise in data governance, architecture, modelling, and platform design.
• Proven experience enabling data products at scale within large, cross-functional organizations.
• Strong business acumen and interest to deeply understand of PayPal’s credit use cases, data producers, and consumer needs.
• Strong technical acumen with the ability to quickly grasp the technical details of products and systems.
• Expertise in data modelling using domain-driven design principles and a strong grasp of data architecture best practices.
• Proficient in building and optimizing data platforms and pipelines using streaming and batch processing, including Lambda and Kappa architectures.
• Technical proficiency data management stacks and techniques such as Python, SQL, BigQuery, Airflow, Spark, Kafka, and data modelling, with experience in integrating ML/AI into scalable data solutions.
• Experience with cloud-based data platforms, preferably GCP, and associated services.
• Demonstrated success in supporting ML/AI workloads (e.g., feature stores, training pipelines, model monitoring) and integrating AI into data workflows.
• Familiarity with AI-enhanced data systems (e.g., automated data discovery, quality monitoring, ML-assisted ETL).
• Experience leveraging ML/AI to manage and optimize data infrastructure, including AIOps for pipeline monitoring, intelligent data quality alerting, automated schema drift detection, and AI-driven resource scaling for data platforms.
• Hands-on understanding of ML/AI model explainability techniques and the ability to build data pipelines and infrastructure that support interpretability requirements, particularly in regulated environments such as credit risk.
• Able to independently drive complex and ambiguous problem-solving efforts, balancing technical and strategic trade-offs.
• Excellent verbal and written communication skills with the ability to collaborate effectively across engineering, risk, product, and compliance teams.
• Adaptive, self-motivated, and comfortable navigating shifting priorities while maintaining focus on the end-to-end impact of data across systems.
• BS or advanced degree in Engineering, Computer Science, or related technical field.

Nice To Haves

• Experience in financial services, with a strong preference for exposure to the credit risk domain.
• Hands-on experience tracking and operating ML/AI evaluation frameworks, including offline evaluation pipelines, benchmark datasets, A/B testing infrastructure, and continuous model performance tracking to ensure production model reliability and fairness.
• Proven experience bridging the gap between data engineering and machine learning engineering.

Responsibilities

• Lead complex data engineering projects, ensuring they meet business objectives and deliver actionable insights.
• Develop advanced data architectures and pipelines to analyze large datasets and solve complex business problems.
• Collaborate with senior leadership to identify data-driven opportunities for business growth and efficiency.
• Implement best practices for data management, analysis, and visualization.
• Ensure data governance and compliance with relevant regulations and standards.
• Provide mentorship and technical guidance to the data engineering team.
• 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 over PB of data and 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.

Benefits

• Comprehensive, choice-based programs, to support all aspects of personal wellbeing—physical, emotional, and financial—delivering meaningful value where it matters most.
• Flexible, balanced work culture.
• Holistic approach to benefits.
• Generous paid time off.
• Healthcare coverage for you and your family.
• Resources to create financial security.
• Resources to support your mental health.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Number of Employees: 5,001-10,000 employees