Analytics Engineer, Data Science

DoorDash USA•Chicago, CT

1d•Remote

About The Position

The Analytics Engineering team at DoorDash is embedded within the Analytics and Data Engineering Orgs, and is responsible for building internal data products that scale decision-making across business teams and drive efficiency in our operations. Data is fundamental to DoorDash’s success, and this team plays a critical role in enabling high-impact, data-driven solutions across Product, Operations, Finance, and more. Please apply here for all non-managerial levels within the following analytics teams: Consumer & Growth Business Operations Dasher & Logistics Customer Experience & Integrity Merchant Ads & Promotions New Verticals. As an Analytics Engineer, you’ll play a key role in building and scaling the data foundations that enable fast, reliable, and actionable insights. You’ll work closely with partner teams to drive end-to-end analytics initiatives, working alongside Data Engineers, Data Scientists, Software Engineers, Product Managers, and Operators. This is a highly technical role where you'll be a driving force behind the analytics stack, delivering trusted data and metrics that support decision-making at all levels of the company. If you're energized by solving technical problems with data and are comfortable being deeply embedded across several domains, this role is for you!

Requirements

2-6+ 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
Experience 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)
Strong communication skills and experience working with technical and non-technical teams
Comfortable working in fast paced environment, self starter and self organizing
Ability to think strategically, analyze and interpret market and consumer information

Responsibilities

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, and generate insights by developing structured solutions to resolve them
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
Be a cross-functional champion at upholding high data integrity standards to increase reusability, readability and standardization