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As an Engineer on the Data Intelligence team, you will be dealing with large-scale data pipelines and data sets that are critical and foundational for Uber to make decisions for a better customer experience. You will be working on a petabyte scale of analytics data from multiple Uber applications. Help us build the software systems and data models that will enable data scientists to understand our user behavior better and thrive on the data-driven mindset at Uber.
Job Responsibility:
Responsible for defining the Source of Truth (SOT) and dataset design for multiple Uber teams
Identify unified data models, collaborating with Data Science teams
Streamline data processing of the original event sources and consolidate them in the source of truth event logs
Build and maintain real-time/batch data pipelines that can consolidate and clean up usage analytics
Build systems that monitor data losses from the different sources and improve the data quality
Own the data quality and reliability of the Tier-1 & Tier-2 datasets, including maintaining their SLAs, TTL, and consumption
Devise strategies to consolidate and compensate for the data losses by correlating different sources
Solve challenging data problems with cutting-edge design and algorithms
Requirements:
3+ years of Data engineering experience
Demonstrated experience of working with large data volumes and backend services
Good working knowledge of SQL (mandatory) and any other languages (Java, Scala, Python)
Working Experience of ETL, Data pipelines, Data Lake, Data Modeling fundamentals
Good problem-solving and analytical skills
Good team player and collaboration skills
Nice to have:
Experience in data engineering and working with Big data
Experience with ETL or Streaming data and one or more of, Kafka, HDFS, Apache Spark, Apache Flink, Hadoop
Good to have experience with backend services and familiarity with one of the cloud platforms (AWS/ Azure / Google /Oracle cloud)