Analytics Engineer

Role Overview

As an Analytics Engineer at DoorDash, you will build and scale data foundations that enable fast, reliable, actionable insights. You’ll work closely with data scientists, data engineers, software engineers, product managers, operators, and business stakeholders to deliver trusted data products and metrics.

Responsibilities

• Collaborate with data scientists, data engineers, and business stakeholders to translate business needs into data requirements.
• Identify key business questions and problems; generate insights through structured analytics solutions.
• Lead the development of data products and self-serve analytics tools to scale analytics across the company.
• Build and maintain canonical datasets by creating high-volume, reliable ETL/ELT pipelines using data lake and data warehousing concepts.
• Design metrics and data visualizations using dashboarding tools such as Tableau, Sigma, and Mode.
• Champion data integrity standards to improve reusability, readability, and standardization.

Requirements

• 3+ years of experience in business intelligence, analytics engineering, data engineering, or a similar role.
• Strong SQL skills for data transformation.
• Comfort with at least one functional/OOP language such as Python or Scala.
• Experience creating reporting and visualizations with Looker/Tableau/Sigma/Mode.
• Familiarity with database/data platform fundamentals such as S3, Trino, Hive, and Spark, plus experience with SQL performance tuning.
• Experience writing data quality checks (e.g., Pydeequ, Great Expectations).
• Strong communication skills with technical and non-technical teams.
• Ability to work in a fast-paced environment as a self-starter.

Nice to Have

• Experience with modern data warehousing platforms such as Snowflake, Databricks, or Redshift.
• Ability to optimize query/performance on data platforms.
• Experience with multi-step ETL and orchestration workflows using Airflow, Dagster, or dbt.
• Familiarity with experimentation concepts such as A/B testing.