Senior Analytics Engineer

As an Analytics Engineer at DoorDash, you will play a key role in building and scaling the data foundations that enable fast, reliable, and actionable insights. You will work closely with partner teams to drive end-to-end analytics initiatives, collaborating with Data Engineers, Data Scientists, Software Engineers, Product Managers, and Operators. Your responsibilities will include: - Collaborating with data scientists, data engineers, and business stakeholders to understand business needs, and translating that scope into data requirements - Identifying key business questions and problems to solve for, and generating insights by developing structured solutions to resolve them - Leading the development of data products and self-serve tools that enable analytics to scale across the company - Building and maintaining canonical datasets by developing high-volume, reliable ETL/ELT pipelines using data lake and data warehousing concepts - Designing metrics and data visualizations with dashboarding tools like Tableau, Sigma, and Mode - Being a cross-functional champion at upholding high data integrity standards to increase reusability, readability, and standardization Qualifications required for this role: - 5+ years of experience working in business intelligence, analytics engineering, data engineering, or a similar role - Strong proficiency in SQL for data transformation, comfort in at least one functional/OOP language such as Python or Scala - Expertise in creating compelling reporting and data visualization solutions using dashboarding tools (e.g., Looker, Tableau, Sigma) - Familiarity with database fundamentals (e.g., S3, Trino, Hive, Spark), and experience with SQL performance tuning - Experience in writing data quality checks to validate data integrity (e.g., Pydeequ, Great Expectations) - Excellent communication skills and experience working with technical and non-technical teams - Comfortable working in a fast-paced environment, self-starter, and self-organizer - Ability to think strategically, analyze, and interpret market and consumer information Additionally, it would be nice to have experience with modern data warehousing platforms (e.g., Snowflake, Databricks, Redshift) and the ability to optimize performance, as well as experience building multi-step ETL jobs coupled with orchestrating workflows (e.g., Airflow, Dagster, DBT) and familiarity with experimentation concepts like A/B testing and their data requirements.,