About the Team

Come help us build the world's most reliable on-demand, logistics engine for delivery! We're bringing on experienced engineers to help us further our 24x7, global infrastructure system that powers DoorDash's three-sided marketplace of consumers, merchants, and dashers.

About the Role

The Data Management team's mission is to build a robust and scalable data storage platform to power high throughput analytics that is easily accessible/cataloged, and compliant with financial and privacy regulations, fostering trust and confidence in our data-driven decision-making process. The enterprise data lakehouse is the next evolution within DoorDash’s data platform to provide a cost-effective alternative to traditional warehousing. The team is relatively small, so there's an opportunity for impact where you can help grow the team and shape the roadmap for the data platform at DoorDash. You will report directly to the Data Management  Engineering Manager. You must be located in the Bay Area, Seattle or New York for this hybrid role.

You're excited about this opportunity because you will… Work on building and maintaining a large scale data lakehouse to enable data builders at DoorDash to deliver high-quality and trustable data sets and metrics Help scale our lakehouse to hundreds of petabytes in a reliable and efficient way leveraging the latest open source lakehouse catalog and table formats Help accelerate the adoption of the lakehouse through seamless integrations with a wide range of compute platforms Come up with solutions for scaling data systems for various business needs Collaborate in a dynamic startup environment We're excited about you because… B.S., M.S., or PhD. in Computer Science or equivalent 2+ years of experience with CS fundamental concepts and experience with at least one of the programming languages of Scala, Java, and Python You are located or are willing to locate to the Bay Area, Seattle or New York Prior technical experience in Big Data infrastructure - you've built meaningful pieces of data infrastructure. Bonus if those were open-sourced technologies like Iceberg, Spark, Airflow, Kafka, Flink Experience improving efficiency, scalability, and stability of data platforms

 

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