The Analytics Engineer role at DoorDash is part of the Analytics Engineering team, which focuses on building internal data products to enhance decision-making across various business teams. This highly technical position involves collaborating with data scientists, engineers, and business stakeholders to develop data solutions that drive efficiency and insights. The role requires a strong foundation in data engineering and analytics, with an emphasis on delivering trusted data and metrics. The successful candidate will be instrumental in scaling analytics initiatives across the company.

• 2-6+ years of experience working in business intelligence, analytics engineering, data engineering, or a similar role.
• Strong proficiency in SQL for data transformation and comfort in at least one functional/OOP language such as Python or Scala.
• Experience in creating compelling reporting and data visualization solutions using dashboarding tools like Looker, Tableau, or Sigma.
• Familiarity with database fundamentals such as S3, Trino, Hive, and Spark, along with experience in SQL performance tuning.
• Experience in writing data quality checks to validate data integrity using tools like Pydeequ or Great Expectations.
• Strong communication skills and experience working with both technical and non-technical teams.
• Ability to work in a fast-paced environment and be a self-starter.

• Collaborate with data scientists, data engineers, and business stakeholders to understand business needs and translate that scope into data requirements.
• Identify key business questions and problems to solve for, generating insights by developing structured solutions.
• Lead the development of data products and self-serve tools that enable analytics to scale across the company.
• Build and maintain canonical datasets by developing high-volume, reliable ETL/ELT pipelines using data lake and data warehousing concepts.
• Design metrics and data visualizations with dashboarding tools like Tableau, Sigma, and Mode.
• Uphold high data integrity standards to increase reusability, readability, and standardization.