DoorDash
About this role
DoorDash is the largest food delivery platform in the United States, and its Data Engineering team builds the database solutions behind reporting, product analytics, marketing optimisation and financial reporting. This role is described as a technical powerhouse to help scale that data infrastructure, automation and tooling. You work with business partners to understand data requirements, work with engineering, product teams and third parties to collect the data, then design and implement large scale, high volume, high performance data models and pipelines for the data lake and warehouse. You also build data quality checks and monitoring, improve the reliability and scalability of ETL processes, and manage a portfolio of data products. The stack is Python or Java, Airflow, Spark, Hive, Druid and Presto with Kafka or Flink for streaming, over Snowflake, Redshift and PostgreSQL on AWS or GCP.
Who this is for
What the posting asks for, and read this carefully because the numbers stack: 5 or more years of professional experience overall, and separately 3 or more years working in data engineering, business intelligence or a similar role. The effective floor is 5 years, not 3. Also required are proficiency using AI coding tools such as Claude Code, Codex or Cursor across the full software development lifecycle from design and code generation through testing, monitoring and release, proficiency in Python or Java, 3 or more years with ETL orchestration and workflow management tools such as Airflow, Flink, Oozie or Azkaban on AWS or GCP, expert level database fundamentals, SQL and distributed computing, 3 or more years with the distributed data ecosystem including Spark, Hive, Druid and Presto plus streaming technologies such as Kafka or Flink, experience with Snowflake, Redshift, PostgreSQL or other database platforms, knowledge of reporting tools such as Tableau, Superset and Looker, and strong communication with both technical and non technical teams.
The real day to day: working with business partners and stakeholders to understand data requirements, working with engineering, product teams and third parties to collect the required data, designing and implementing large scale high volume high performance data models and pipelines for the data lake and warehouse, developing data quality checks and monitoring routines, improving the reliability and scalability of ETL processes, managing a portfolio of data products that deliver trustworthy data, and helping onboard and support engineers joining the team.
Location and setup: Hyderabad, India. The posting states that you must be located near one of DoorDash's engineering hubs, so this is not a remote role despite being on a large distributed company's board.
Honest fit guidance: the explicit requirement for proficiency with AI coding tools is worth noting, since it is listed as a requirement rather than a nice to have and names specific products. This is a good example of why reading the experience sentence by hand matters: an automated parser would report this role as 3 years because that number appears three times, when the actual floor is 5. Strong choice if you want data engineering at genuine consumer scale, and DoorDash's data organisation is explicitly the foundation for company decision making rather than a reporting function.
The real day to day: working with business partners and stakeholders to understand data requirements, working with engineering, product teams and third parties to collect the required data, designing and implementing large scale high volume high performance data models and pipelines for the data lake and warehouse, developing data quality checks and monitoring routines, improving the reliability and scalability of ETL processes, managing a portfolio of data products that deliver trustworthy data, and helping onboard and support engineers joining the team.
Location and setup: Hyderabad, India. The posting states that you must be located near one of DoorDash's engineering hubs, so this is not a remote role despite being on a large distributed company's board.
Honest fit guidance: the explicit requirement for proficiency with AI coding tools is worth noting, since it is listed as a requirement rather than a nice to have and names specific products. This is a good example of why reading the experience sentence by hand matters: an automated parser would report this role as 3 years because that number appears three times, when the actual floor is 5. Strong choice if you want data engineering at genuine consumer scale, and DoorDash's data organisation is explicitly the foundation for company decision making rather than a reporting function.
Apply on company site
Opens job-boards.greenhouse.io, the employer's own application page. Applying is always free.