Data Engineer Ii Global Intelligence

Toronto, ON, Canada

Job Description


The Global Intelligence Team focuses on making Uber take important marketplace decisions with better data and algorithms. The ambitious problems include modeling complex market-level dynamics, rider and driver choices, cross-service decisions across rides/eats, and fine-tuning Uber\'s pricing with data/algorithms from this team. The software engineers on the team use substantial amounts of data to address these challenges, building scalable engineering solutions. We are looking for people who are passionate about solving ambitious business/product issues with well-trained data engineering expertise, with prior experience or interest in science models and methodologies and who are also passionate about seeking the truth via deep-diving into the complicated structured and unstructured data.

What You\'ll Do:

  • Work on creating a platform that powers data driven decision making for Uber Rides and Eats line of business.
  • Work very closely with the science team to implement and productionize the models.
  • Design and develop new systems to empower fast data-driven decisions
  • Build distributed backend systems serving real-time analytics and machine learning features at Uber scale.
  • Work with the product and science team to build and drive technical roadmap and vision for the team.
Basic Qualifications:
  • 2+ years of full-time engineering experience
  • Experience working with multiple cross functional teams (product, science,product ops etc)
  • Understanding of Big data architecture, ETL frameworks and platforms.
  • Expertise in one or more object-oriented programming languages (e.g. Python, Go, Java, C++) and the eagerness to learn more.
  • Experience with data-driven architecture and systems design
  • Knowledge of Big Data Technologies
  • Proven experience in large-scale distributed storage and database systems (SQL or NoSQL, e.g. MySQL, Cassandra) and data warehousing architecture and data modeling.
Preferred Qualifications:
  • Experience or interest in learning science models and methodologies.
  • Experience building complex systems and knowledge of Hadoop related technologies such as HDFS, Kafka, Hive, and Presto.
  • A passion for taking ownership. You pride yourself on efficient monitoring, strong documentation, and proper test coverage and you call something \xe2\x80\x9cdone\xe2\x80\x9d only when these are in place.
  • Building and earning respect within the team and peers. You believe that you can achieve more on a team - that the whole is greater than the sum of its parts. You rely on others\' candid feedback for continuous improvement and you help others by returning the favor.
  • BS/MS/Phd in Computer Science or related field required
We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let\'s move the world forward, together.

Offices continue to be central to collaboration and Uber\'s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.

*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to .

Uber is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, Veteran Status, or any other characteristic protected by law.

Uber

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Job Detail

  • Job Id
    JD2229123
  • Industry
    Not mentioned
  • Total Positions
    1
  • Job Type:
    Full Time
  • Salary:
    Not mentioned
  • Employment Status
    Permanent
  • Job Location
    Toronto, ON, Canada
  • Education
    Not mentioned