Uber

Sr Data Engineer - Marketplace Pricing & Incentive...

Job description

About the role:

Partners with stakeholders to translate complex business or technical problems into end-to-end data tools and solutions (e.g., pipelines, models, tables). This role also leads team efforts to design and develop large-scale data systems (e.g., databases, data warehouses, big data systems), platforms, and infrastructure for various analytics and business applications.


About the Team:

The Marketplace Pricing & Incentives team is responsible for Uber’s profitability and growth story. We focus on how to intelligently price a ride trip or a delivery order to make sure Uber can achieve profitability while keeping a steady growth of business. The team is critical in fostering a healthy ride-sharing & delivery ecosystem via a balanced marketplace and in providing a pleasant and sticky experience for Uber’s customers. We seek the best opportunities to grow Uber's business with the state-of-the-art technology (machine learning, constrained optimization, distributed platform, etc.) to achieve industry leading ROI.


Minimum qualifications:

  • PhD or equivalent in Computer Science, Engineering, Mathematics or related field OR 3-years full-time Software Engineering work experience, WHICH INCLUDES 2-years total technical software engineering experience in one or more of the following areas:
    • Programming and scripting language (e.g. Python, SQL, Java/Scala)
    • Big data frameworks (e.g. Spark, Flink, Presto), data modeling, and writing ETLs
    • Designing end-to-end data solutions and architecture


  • Note the 2-years total of specialized software engineering experience may have been gained through education and full-time work experience, additional training, coursework, research, or similar (OR some combination of these). The years of specialized experience are not necessarily in addition to the years of Education & full-time work experience indicated.


Technical skills:

Required:

  • Data Analytics
  • Data Modeling
  • Big Data Processing
  • Data Storage Systems
  • Distributed Systems

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