Analytics Engineer

As an Analytics Engineer at DoorDash, you will play a crucial role in building and scaling data foundations to provide fast, reliable, and actionable insights. You will collaborate with data scientists, data engineers, and business stakeholders to understand business needs and translate them into data requirements. Your responsibilities will include identifying key business questions, developing structured solutions to solve them, leading the development of data products and self-serve tools, and maintaining canonical datasets through high-volume, reliable ETL/ELT pipelines using data lake and data warehousing concepts. Key Responsibilities: - Collaborate with data scientists, data engineers, and business stakeholders to understand and translate business needs into data requirements - Identify key business questions and develop structured solutions to generate insights - Lead the development of data products and self-serve tools for scaling analytics across the company - Build and maintain canonical datasets through reliable ETL/ELT pipelines using data lake and data warehousing concepts - Design metrics and data visualizations using dashboarding tools like Tableau, Sigma, and Mode - Uphold high data integrity standards to increase reusability, readability, and standardization Qualifications Required: - 5+ years of experience in business intelligence, analytics engineering, data engineering, or similar roles - Proficiency in SQL for data transformation and familiarity with functional/OOP languages like Python or Scala - Expertise in creating compelling reporting and data visualizations using dashboarding tools (e.g., Looker, Tableau, Sigma) - Familiarity with database fundamentals (e.g., S3, Trino, Hive, Spark) and experience in SQL performance tuning - Experience in writing data quality checks to validate data integrity (e.g., Pydeequ, Great Expectations) - Strong 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 Nice to Have: - Experience with modern data warehousing platforms (e.g., Snowflake, Databricks, Redshift) and ability to optimize performance - Experience in building multi-step ETL jobs and orchestrating workflows (e.g., Airflow, Dagster, DBT) - Familiarity with experimentation concepts like A/B testing and their data requirements If you're energized by solving technical problems with data and enjoy being deeply embedded across various domains, this role at DoorDash is a great fit for you. The company is committed to supporting employees' happiness, healthiness, and overall well-being by providing comprehensive benefits and perks. DoorDash values diversity and inclusion, fostering a community of diverse teams from various backgrounds, experiences, and perspectives to drive innovation. As an Analytics Engineer at DoorDash, you will play a crucial role in building and scaling data foundations to provide fast, reliable, and actionable insights. You will collaborate with data scientists, data engineers, and business stakeholders to understand business needs and translate them into data requirements. Your responsibilities will include identifying key business questions, developing structured solutions to solve them, leading the development of data products and self-serve tools, and maintaining canonical datasets through high-volume, reliable ETL/ELT pipelines using data lake and data warehousing concepts. Key Responsibilities: - Collaborate with data scientists, data engineers, and business stakeholders to understand and translate business needs into data requirements - Identify key business questions and develop structured solutions to generate insights - Lead the development of data products and self-serve tools for scaling analytics across the company - Build and maintain canonical datasets through reliable ETL/ELT pipelines using data lake and data warehousing concepts - Design metrics and data visualizations using dashboarding tools like Tableau, Sigma, and Mode - Uphold high data integrity standards to increase reusability, readability, and standardization Qualifications Required: - 5+ years of experience in business intelligence, analytics engineering, data engineering, or similar roles - Proficiency in SQL for data transformation and familiarity with functional/OOP languages like Python or Scala - Expertise in creating compelling reporting and data visualizations using dashboarding tools (e.g., Looker, Tableau, Sigma) - Familiarity with database fundamentals (e.g., S3, Trino, Hive, Spark) and experience in SQL performance tuning - Experience in writing data quality checks to validate data integrity (e.g., Pydeequ, Great Expectations) - Strong communication skills and experience working with technical and non-technical teams - Comfortable working in a fast-paced environment, self-starte