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Lead Software Engineer – Risk Solutions

LinkedIn PayPal San Jose, CA
Not Applicable Posted April 4, 2026 2 variants Job link
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Requirements
  • 8+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.
  • Additional Responsibilities And Preferred Qualifications Key Responsibilities Lead architecture and delivery of intelligent systems supporting PayPal's Risk and Fraud platforms — with strong emphasis on cloud, data, and backend service design.
  • Build and maintain real-time and batch data pipelines, APIs, and event-driven services operating at petabyte scale and sub-second latency.
  • Champion cloud-native engineering practices, especially within Google Cloud Platform (GCP) and big data ecosystems (e.g., BigQuery, Dataproc, Pub/Sub, Airflow).
  • Integrate AI-assisted development tools into the engineering lifecycle (e.g., test-generation bots, RAG-based copilots, code accelerators) to boost developer efficiency and reliability.
  • Partner closely with Product, Design, Data Science, and Analytics to translate domain goals into robust, scalable technical solutions.
  • Provide technical leadership across the SDLC: architecture reviews, coding standards, security practices, scalability assessments, and release readiness.
  • Drive engineering best practices: clean architecture, CI/CD automation, observability, and reusable component design.
  • Mentor and support junior engineers, cultivating a culture of learning, ownership, and craftsmanship.
  • Collaborate across teams to align on platform strategy and enable extensibility and reuse in shared infrastructure.
  • Required Skills and Qualifications 10+ years of software engineering experience, including ownership of distributed, data-intensive systems in large-scale production environments.
  • Strong expertise in cloud-native engineering, with a preference for GCP.
  • Hands-on experience with BigQuery, Pub/Sub, Dataproc, Cloud Functions, or equivalent services.
  • Proficient in backend development using Java, Python, or Node.js, with a deep understanding of microservices and service orchestration.
  • Hands-on experience with Big Data frameworks: Apache Spark, Kafka, Hadoop, Hive, and Airflow.
  • Experience designing and optimizing streaming architectures and near real-time processing systems.
  • Strong understanding of data modeling, SQL and NoSQL databases (e.g., MySQL, MongoDB, Redis), and performance tuning at scale.
  • Exposure to AI-assisted engineering tools or strong interest in leveraging such tools to improve software delivery.
  • Solid grounding in computer science fundamentals: data structures, algorithms, object-oriented design, operating systems.
  • Familiarity with DevOps, including CI/CD pipelines, automation, and performance monitoring.
  • Excellent communication and collaboration skills; experience working across cross-functional teams.
  • Preferred Qualifications Prior experience building or integrating with Risk, Fraud, or Trust & Safety systems at global scale.
  • Exposure to agentic AI tools or developer copilots such as code-generation agents, test automation bots, or RAG-based assistants.
  • Familiarity with AI/ML pipeline integration, such as feature stores, inference orchestration, or model monitoring (as a consumer).
  • Knowledge of stream processing architectures (e.g., Kafka Streams, Apache Beam, Flink).
  • Experience building shared platform components, SDKs, or internal frameworks across teams or domains.
  • Awareness of privacy-preserving engineering, security best practices, and compliance-oriented systems.
  • Ability to influence architecture decisions and lead cross-cutting technical efforts across multiple teams.
  • Why This Role Matters This role is pivotal to shaping PayPal's future in AI-enabled Risk and Fraud engineering.
  • You will be at the forefront of building trusted, secure, and intelligent platforms that scale globally.
Preferred Skills
  • Strong expertise in cloud-native engineering, with a preference for GCP.
  • Hands-on experience with BigQuery, Pub/Sub, Dataproc, Cloud Functions, or equivalent services.
  • Proficient in backend development using Java, Python, or Node.js, with a deep understanding of microservices and service orchestration.
  • Hands-on experience with Big Data frameworks: Apache Spark, Kafka, Hadoop, Hive, and Airflow.
  • Strong understanding of data modeling, SQL and NoSQL databases (e.g., MySQL, MongoDB, Redis), and performance tuning at scale.
  • Exposure to AI-assisted engineering tools or strong interest in leveraging such tools to improve software delivery.
  • Solid grounding in computer science fundamentals: data structures, algorithms, object-oriented design, operating systems.
  • Familiarity with DevOps, including CI/CD pipelines, automation, and performance monitoring.
  • Excellent communication and collaboration skills; experience working across cross-functional teams.
  • Preferred Qualifications Prior experience building or integrating with Risk, Fraud, or Trust & Safety systems at global scale.
  • Exposure to agentic AI tools or developer copilots such as code-generation agents, test automation bots, or RAG-based assistants.
  • Familiarity with AI/ML pipeline integration, such as feature stores, inference orchestration, or model monitoring (as a consumer).
  • Knowledge of stream processing architectures (e.g., Kafka Streams, Apache Beam, Flink).
  • Experience building shared platform components, SDKs, or internal frameworks across teams or domains.
  • Awareness of privacy-preserving engineering, security best practices, and compliance-oriented systems.
  • Ability to influence architecture decisions and lead cross-cutting technical efforts across multiple teams.
  • Why This Role Matters This role is pivotal to shaping PayPal's future in AI-enabled Risk and Fraud engineering.
  • You will be at the forefront of building trusted, secure, and intelligent platforms that scale globally.
Education
  • (Not required) – 8+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.