Senior & SDE II Software Engineer, Data Platform (All Teams)

<div class="content-intro"><p><img style="display: none; max-width: 100%;" src="https://click.appcast.io/greenhouse-te8/a31.png?ent=34e=22630t=1701374353806" width="1px"> <img style="display: none; max-width: 100%;" src="https://track.jobadx.com/v1/i.gif?utm_pixel=224e990b-8ff4-4287-8d5d-2ff09647f181utm_ptz=ESTutm_rqt=track" alt="" width="1"></p></div><h2><strong>About the Team</strong></h2> <p>DoorDash is a data driven organization and relies on timely, accurate and reliable data to drive many business and product decisions. The Core Data Platform organization owns all the infrastructure necessary to run an operationally efficient analytical data stack. </p> <h2><strong>About the Roles </strong></h2> <p>The Data Platform team spans data mobility frameworks, ingestion, infrastructure, tools, and governance. Together, they design and operate scalable compute and ingestion frameworks using technologies such as Spark, Flink, Kafka, Airflow, and modern lakehouse solutions, while also building abstractions and tools that simplify data workflows for engineers, analysts, and ML practitioners. In parallel, these teams establish strong data quality, cataloging, privacy, and compliance standards to ensure trust in analytics and regulatory adherence. As relatively high-impact teams, they offer engineers the opportunity to shape the roadmap, influence core platform decisions, and directly enable DoorDash’s business-critical insights and real-time personalization capabilities. You must be located in San Francisco, CA, Sunnyvale, CA, Seattle, WA, or New York, NY.</p> <h2><strong>You're excited about this opportunity because you will…</strong></h2> <ul> <li>Drive vision strategy for building the frameworks charter and position it to handle the challenges of a rapidly growing business.</li> <li>Scale the analytical platform for the increasing amounts of data and use cases. </li> <li>You will bring your expertise in building and operating high scale systems with a focus on reliability, scalability and cost efficiency.</li> <li>Collaborate with stakeholders building solutions on top of the platform</li> <li>Foster a positive and supportive work culture, upleveling others.</li> </ul> <h2><strong>We're excited about you because you have… </strong></h2> <ul> <li>B.S., M.S., or PhD. in Computer Science or equivalent.</li> <li>2+ years of industry experience at our I4 level, 5+ years of industry experience at our I5 level</li> <li>Experience working on big data domains (streaming, batch, data lake OLAP) and promoting realtime insights and data analytics at scale using technologies such as Apache Kafka, Flink, Spark,, Cassandra,, Clickhouse, Iceberg etc.</li> <li>You are located or are willing to locate to the Bay Area or Seattle</li> <li>Prior technical experience in Big Data infrastructure governance - you've built meaningful pieces of data infrastructure. Bonus if those were open-sourced technologies like DataHub, Spark, Airflow, Kafka, Flink</li> <li>Experience improving efficiency, scalability, and stability of data platforms</li> <li data-prosemirror-content-type="node" data-prosemirror-node-name="heading" data-prosemirror-node-block="true" data-pm-slice="0 0 []">You are located or willing to relocate to the Bay Area, Seattle, or New York</li> </ul> <p> </p> <p style="font-weight: 500;">Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only</p> <p>We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using <a href="https://getcovey.com/product/covey-scout-inbound">Covey Scout for Inbound</a> from August 21, 2023, through December 21, 2023, and resumed using <a href="https://getcovey.com/product/covey-scout-inbound">Covey Scout for Inbound</a> again on June 29, 2024.</p> <p>The Covey tool has been ... (truncated, view full listing at source)