Staff Software Engineer - Maps

Uber Uber · Consumer · Amsterdam, Netherlands · Engineering

Staff Software Engineer role focused on building and maintaining large-scale, real-time data pipelines for Uber's Maps traffic domain. The role involves leading complex technical initiatives, solving high-impact problems, and owning work end-to-end, with a focus on performance, safety, and system architecture. Requires experience with data pipelines, system performance analysis, and leading technical initiatives. Machine learning experience and traffic domain expertise are preferred.

What you'd actually do

  1. Design, build, and maintain data pipelines that process real-time road data at a global scale, where every millisecond of latency impacts millions of ETAs.
  2. Lead technically complex initiatives, such as re-architecting our incident and closure detection systems to improve accuracy and reaction time.
  3. Solve messy, high-impact problems—like optimizing the interface between traffic and routing—often without a clear starting point or obvious solution.
  4. Navigate the trade-offs between short-term tactical fixes and long-term architectural stability while keeping our Maps ecosystem running smoothly.
  5. Own your work end-to-end, from drafting the multi-year technical vision for traffic domains to debugging production issues when the stakes are high.

Skills

Required

  • building and maintaining high-scale data pipelines
  • Flink or Spark
  • data analysis techniques
  • reason about system performance, headroom, and data quality
  • leading major technical initiatives from inception through to production and maintenance
  • work across multiple technical domains in parallel
  • lead through others, driving results from more junior engineers on the team

Nice to have

  • machine learning systems
  • end-to-end ML lifecycle
  • traffic modeling
  • ETA prediction
  • route optimization
  • real-time geospatial data
  • proactive, entrepreneurial mindset
  • identifying impactful projects and driving them to success
  • Systems thinking approach to reducing latency and improving reliability in distributed environments

What the JD emphasized

  • real-time road data
  • massive scale
  • performance and safety
  • technical debt
  • high-autonomy environments
  • technical vision
  • high-scale data pipelines
  • system performance
  • data quality
  • major technical initiatives
  • technical domains
  • lead through others
  • machine learning systems
  • traffic modeling
  • ETA prediction
  • route optimization
  • real-time geospatial data
  • Systems thinking approach
  • reducing latency
  • improving reliability