Staff Software Engineer (Data)
The Global Intelligence Team focuses on improving Uber network efficiency and relative position in the category with better data and algorithms. The ambitious problems include benchmarking Uber's position in the category, 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 data engineers on the team use substantial amounts of internal and external 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 and who are passionate about seeking the truth via deep-diving into the complicated structured and unstructured data.
- 10+ years of experience building complex systems.
- Expertise in one or more object-oriented programming languages (e.g. Python, Go, Java, C/C++) and zeal to learn more. Experience with data-driven architecture and systems design knowledge of Hadoop-related technologies such as HDFS, Apache Spark, Apache Flink, Hive, and Presto.
- Proven experience in large-scale distributed storage and database systems (SQL or NoSQL, e.g. MySQL, Cassandra) and data warehousing architecture and data modelling.
- Good problem-solving and analytical skills. Good team player and collaboration skills.
- Passion to take ownership & responsibility.
--- Candidate will do---
- You will design, code, test, and launch new data engineering pipelines and ML-based product features at global scale.
- Work on creating a Competitive Intelligence platform for Uber Rides, Eats/Delivery line of businesses. 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.
- Build and maintain real-time/batch data pipelines that can consolidate and clean up usage analytics. Solve challenging data problems with cutting edge design and algorithms.
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