Software Engineer
Company Name:-
Uber
Job Location:-
Bengaluru, Karnataka
Job Summary:-
About the Role
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 better customer experience.
You will be working on a petabyte scale of analytics data from the 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.
About the team
The Data Intelligence Platform team is responsible for the designing core foundational data sets that are critical to understand customers needs and helps business teams take right decisions in solving these critical problems.
The teams mission is to ensure high quality for all the critical data flows for analytics purposes across all verticals in Uber and enable faster implementation of data needs by building standardized tools and framework for accurate analysis.
We are currently revamping all critical analytical data flows across domains to build high quality data sets and frameworks that are used across Uber.
Basic Qualifications5+ years of backend software engineering experienceExpertise in one or more object-oriented programming languages (Go, Java, Python).
Demonstrated experience of working with large data volumes and backend services.
Preferred Qualifications
Solid experience developing systems at Scale
Experience working in Data Engineering ( ETL or Streaming data) , HDFS, Apache Spark , Apache Flink , Hadoop.
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Good to have experience building backend services and familiarity with one of the cloud platform ( AWS/ Azure / Google cloud)
What the Candidate Will DoResponsible for defining the Data Architecture for multiple Uber teams.
Identify unified data models collaborating with Data Science teamsStreamline data processing of the original event sources and consolidate them in source of truth event logsBuild and maintain real-time/batch data pipelines that can consolidate and clean up usage analyticsBuild systems that monitor data losses from the mobile sources and improve the data qualityDevise strategies to consolidate and compensate the data losses by correlating different sourcesSolve challenging data probl
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