Google

Google

Technical Lead, YouTube Ads Data Infrastructure

Kirkland, WA, USA

Sponsorship not specified$207k-$301kDetected 13 hours ago
Full-Stack DevelopmentData AnalysisNLPUI DesignLeadership

About the role

  • - Bachelor's degree or equivalent practical experience.
  • - 8 years of experience programming in C++.

Responsibilities

  • Drive execution of high-impact, visible data projects, establishing the technical strategy and investment road-map for sustainable YouTube Ads growth.
  • Deliver critical data solutions, including low-latency streaming datasets and improving end-to-end log freshness from over 40 hours to under 6 hours.
  • Collaborate with executive leaders and product teams across the organization to unify workflows, ensure compliance, and maximize Return on Investment (ROI).
  • Guide and mentor team engineers while directly contributing to technical design, coding, and solving ambiguous, complex technical problems.

Requirements

  • Bachelor's degree or equivalent practical experience.
  • 8 years of experience programming in C++.
  • 3 years of experience with software design and architecture.
  • 5 years of experience building and developing large-scale infrastructure and large-scale distributed systems.
  • Experience with storage systems.

Nice to have

  • Master's degree or PhD in Engineering, Computer Science, or a related technical field.
  • 3 years of experience in a technical leadership role leading project teams and setting technical direction.
  • 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
  • Experience in people management.
  • Experience in data analytics, preferably with Google analytics.
  • Experience working with compute infrastructure.
  • Our products need to handle information at massive scale, and extend well beyond web search.
  • the list goes on and is growing every day.

Compensation

  • US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits

This listing is sourced directly from Google's careers page and normalized into a canonical job model.