Software Engineer, Data Mobility - job 1 of 2

As a Software Engineer, Data Mobility at DoorDash, you will be responsible for managing the seamless transfer of data from various sources to our data lakehouse. Your role will include leveraging cutting-edge technologies like Apache Spark, Flink, and Kafka to build a scalable data ingestion framework that supports DoorDash’s global infrastructure. You will also collaborate with cross-functional teams to ensure high-quality data integration and support multiple business lines, driving critical machine learning models and fast-paced experimentation.

To qualify for the Software Engineer, Data Mobility position at DoorDash, candidates should hold a B.S., M.S., or PhD in Computer Science or a related field. You should have at least 2 years of experience in computer science fundamentals and be proficient in one of the programming languages such as Scala, Java, or Python, alongside a solid understanding of SQL. Relevant prior experience in Big Data solutions or open-source frameworks is a strong plus, as is a willingness to relocate to Seattle or the Bay Area.

A Software Engineer, Data Mobility at DoorDash should be familiar with a variety of technologies pivotal for data ingestion, including Apache Spark, Flink, Kafka, Airflow, Delta Lake, and Iceberg. This role involves working extensively with these open-source tools to build a robust data infrastructure capable of handling increasing data volumes effectively.

At DoorDash, Software Engineers, particularly in roles like Data Mobility, can expect significant career growth. The company fosters a culture of innovation and excellence, encouraging team members to take on impactful projects that enhance their skills and visibility within the organization. With various opportunities for learning and development, you can advance your career while contributing meaningfully to a rapidly growing tech company.

The team culture for the Software Engineer, Data Mobility role at DoorDash is collaborative and dynamic. Engineers work closely with cross-functional teams, including Analytics and Product, to foster an environment of shared knowledge and mutual pursuit of excellence. DoorDash values diversity and inclusion, encouraging a variety of perspectives to propel innovation and problem-solving in the fast-paced logistics sector.

When answering this question, highlight specific technologies you've used, such as Apache Spark or Kafka. Discuss projects where you successfully implemented these technologies to solve real-world data challenges, focusing on your understanding of their capabilities and limitations.

To answer this question, outline your strategies for maintaining data quality, such as validation checks, testing protocols, and the use of automated tools. Illustrate your process with a past example where you identified and resolved data quality issues effectively.

Discuss your methods for prioritization, such as using project management tools or categorizing tasks by urgency and impact. Provide an example of a time when you successfully managed competing priorities and delivered high-quality results.

Choose a specific challenge that illustrates your problem-solving skills. Explain the problem's context, your analysis process, the solution you implemented, and the outcome. Highlight any technologies used during the process.

Define scalability and emphasize its importance in data systems, especially in the context of handling increasing volumes of data. Provide examples of how you've designed systems to be scalable or optimized for performance when handling larger datasets.

Elaborate on your experiences collaborating with teams from different departments, such as product management or data analytics. Highlight examples where these collaborations led to improved outcomes and how you communicated effectively across varied functions.

Discuss your contributions to any open-source projects, including the specific role you played, the technologies involved, and the impact your work had on the project. Mention any collaboration tools or methodologies you used in the process.

Talk about your commitment to continuous learning through resources such as online courses, webinars, tech meetups, or following industry thought leaders. Provide specific examples of how you've recently learned and applied new technologies in your work.

Explain your approach to performance tuning, including monitoring key metrics, identifying bottlenecks, and optimizing queries or system configurations. Provide a relevant example showcasing your tuning efforts and the resulting performance gains.

Outline your response strategy, such as identifying the cause of the crash, using monitoring tools to analyze the issue, and implementing failover mechanisms to restore functionality. Emphasize the importance of maintaining data integrity and minimal downtime.