Software Engineer II, Data Engineering

As a Software Engineer II at DoorDash, you'll be responsible for designing, developing, and implementing large-scale data models and pipelines for our Data Lake and Data Warehouse. You'll work closely with business partners to understand their data requirements and collaborate with engineering and product teams to gather the necessary data. Additionally, you'll manage a portfolio of data products, ensuring high-quality data and improving ETL processes while supporting fellow engineers in onboarding.

To apply for the Software Engineer II position at DoorDash, you should have at least 3 years of professional experience in data engineering or a related field. Proficiency in programming languages like Python or Java is essential, along with extensive experience in ETL orchestration and workflow management tools such as Airflow or AWS/GCP. A solid understanding of SQL and distributed computing systems is also crucial, alongside experience working with data warehouse platforms like Snowflake or Redshift.

Software Engineer IIs at DoorDash work with a variety of data tools and technologies, including database systems like PostgreSQL, distributed computing ecosystems such as Spark and Hive, and streaming technologies including Kafka and Flink. Additionally, they utilize reporting tools like Tableau and Superset to analyze data and derive insights that inform business strategies.

The team culture for Software Engineers at DoorDash is collaborative and fast-paced. Engineers are encouraged to share their unique perspectives, innovate, and tackle new challenges while being supported in their career growth. With a commitment to diversity and inclusion, everyone has a voice at the table, which fosters a vibrant community where you can thrive professionally and personally.

At DoorDash, there's ample opportunity for career growth as a Software Engineer II. You'll work in an environment that encourages continuous learning and development. By mentoring new engineers, taking on challenging projects, and enhancing your technical skills with emerging tools and best practices, you can further your career while substantially contributing to the growth of the company.

In answering this question, you should highlight specific ETL processes you have worked on, the tools you used (like Airflow or AWS), and any challenges you faced along with the solutions you implemented. Emphasize your understanding of data quality checks and validations, as well as how you manage data pipelines for reliability and scalability.

Discuss the various data modeling techniques you have used such as entity-relationship diagrams or star/snowflake schemas. Provide examples of projects where you've applied these techniques and how they benefited the data architecture or improved performance in data retrieval.

You should speak to specific data quality checks you have implemented, such as validation rules or anomaly detection methods. Illustrating your commitment to maintaining trustworthy data by outlining any monitoring routines you've established can also demonstrate your thorough approach to quality assurance.

Choose a specific, quantifiable challenge you faced related to data processing or model performance. Describe the steps you took to analyze the issue, the approach you implemented to solve it, and the measurable impact it had on the project results.

Discuss your communication and project management style. Highlight the tools you use for collaboration (like JIRA or Slack) and describe a successful project where you worked alongside product teams or stakeholders, emphasizing the importance of understanding their data needs.

Be prepared to discuss languages such as Python or Java in the context of specific projects. Share examples of how you've used these languages for data processing, automation, or building ETL pipelines, demonstrating your hands-on experience and problem-solving skills.

Discuss specific resources or communities you engage with, such as attending webinars, following industry blogs, participating in relevant discussions on platforms like GitHub, or subscribing to professional newsletters. Highlighting your commitment to continuous learning will resonate well.

Discuss your experience with platforms like AWS or GCP and how you've utilized their services for data storage, processing, or scalability. Reference specific projects where cloud services played a crucial role in achieving high performance and reliability.

Provide examples of specific distributed computing frameworks you've used, such as Spark or Flink. Discuss how you've leveraged these systems to handle large data sets or achieve higher processing speeds, along with any optimization strategies you've implemented.

Share your approach to prioritizing tasks using frameworks such as the Eisenhower matrix or Agile methodologies. Describe how you balance long-term projects with immediate deadlines, ensuring that you keep stakeholders informed throughout the process.