Senior Software Engineer, Payments Data Platform

Google Google · Big Tech · Singapore

This role focuses on building and evolving the core data infrastructure and tooling for Google's Payments Data Platform (GDP). The platform supports advanced analytics, AI personalization, and agentic experiences, aiming to unlock the potential of Payments data for reliable, compliant, and scalable data infrastructure. The engineer will design and implement data pipelines, own technical areas, and drive technical strategy for data management.

What you'd actually do

  1. Design, develop, and implement highly scalable and reliable data pipelines and infrastructure.
  2. Take full ownership of complex technical problems, from ideation and research to proposing and delivering end-to-end solutions, often involving ambiguous requirements.
  3. Drive technical strategy and best practices for data ingestion, processing, storage, and consumption, ensuring data quality, compliance (e.g., data localization, privacy), and discoverability.
  4. Collaborate closely with Product Managers, Data Scientists, Analysts, and Feature Engineering teams to understand data needs and deliver robust data solutions that meet business requirements.
  5. Identify opportunities to improve system efficiency, reliability, and developer experience within the data platform, proactively addressing technical debt and implementing optimizations. Advocate for and implement best practices in data infrastructure, software development, testing, and monitoring to ensure the reliability, scalability, and efficiency of GDP's systems.

Skills

Required

  • Java
  • C/C++
  • Python
  • Objective C
  • JavaScript
  • Go
  • software design and architecture
  • testing
  • maintaining
  • launching software products

Nice to have

  • Master's degree or PhD in Computer Science
  • large-scale data processing systems
  • data platforms
  • distributed systems
  • data warehousing
  • big data technologies
  • Google's internal data technologies
  • Payment products

What the JD emphasized

  • highly scalable and reliable data pipelines and infrastructure
  • complex technical problems
  • ambiguous requirements
  • data quality, compliance (e.g., data localization, privacy), and discoverability
  • robust data solutions

Other signals

  • data pipelines
  • data infrastructure
  • AI personalization
  • agentic experiences
  • Payments data