Data & Storage Systems Engineer II

Uber; San Francisco (CA); Hybrid; USD 171,000 - 209,000; Full time

Benefits offered by this job

Bonus program

Benefits

Job summary

Uber is seeking a Software Engineer II on the Data and Storage teams to architect foundational infrastructure powering business decisions for millions of users. You will design and operate scalable storage and data processing systems, leveraging Spark, Hive, Presto, and Flink to build robust data pipelines across the ad tech stack.

You will collaborate with cross-functional teams and leverage internal Uber tech stacks to productionize winning strategies, while building tools to improve

Qualifications

• 3+ years of professional software engineering experience in backend or data engineering.
• Experience with data modeling, query optimization, and translating customer problems into system design.
• Bachelor's Degree in Computer Science, Engineering, or related field.

Responsibilities

• Design, build and deliver end-to-end features across the AdTech stack.
• Design, develop and operate scalable and reliable services.
• Build scalable data pipelines using Spark, Hive, Presto, Flink.
• Collaborate with Product Management, Data Science, and Marketing.
• Leverage Uber internal tech stacks and systems to shape roadmaps.
• Develop tools and automation to improve end-user efficiency.
• May telecommute.

Skills

Backend development

Distributed systems

Data engineering

Go

Java

Python

C++

GIT

SQL

Spark

Hive

Presto

Flink

Kafka

Education

Bachelor's Degree in Computer Science, Engineering or related field

Tools

GIT

SVN

Hadoop

Hive

Spark

Presto

Kafka

Job description

Uber is seeking a Software Engineer II on the Data and Storage teams to architect foundational infrastructure powering business decisions for millions of users. You will design and operate scalable storage and data processing systems, leveraging Spark, Hive, Presto, and Flink to build robust data pipelines across the ad tech stack.

You will collaborate with cross-functional teams and leverage internal Uber tech stacks to productionize winning strategies, while building tools to improve