The team at DoorDash has a complex set of optimization challenges to deal with using data that they collect from a multi-sided marketplace. In order to handle the volume and variety of information that they use to run and improve the business the data team has to build a platform that analysts and data scientists can use in a self-service manner. In this episode the head of data platform for DoorDash, Sudhir Tonse, discusses the technologies that they are using, the approach that they take to adding new systems, and how they think about priorities for what to support for the whole company vs what to leave as a specialized concern for a single team. This is a valuable look at how to manage a large and growing data platform with that supports a variety of teams with varied and evolving needs.
Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I’m interviewing Sudhir Tonse about how the team at DoorDash designed their data platform
How did you get involved in the area of data management?
Can you start by giving a quick overview of what you do at DoorDash?
What are some of the ways that data is used to power the business?
How has the pandemic affected the scale and volatility of the data that you are working with?
Can you describe the type(s) of data that you are working with?
What are the primary sources of data that you collect?
What secondary or third party sources of information do you rely on?
Can you give an overview of the collection process for that data?
In selecting the technologies for the various components in your data stack, what are the primary factors that you consider when evaluating the build vs. buy decision?
In your recent post about how you are scaling the capabilities and capacity of your data platform you mentioned the concept of maintaining a “paved path” of supported technologies to simplify integration across teams. What are the technologies that you use and rely on for the “paved path”?
How are you managing quality and consistency of your data across its lifecycle?
What are some of the specific data quality solutions that you have integrated into the platform and “paved path”?
What are some of the technologies that were used early on at DoorDash that failed to keep up as the business scaled?
How do you manage the migration path for adopting new technologies or techniques?
In the same post you mentioned the tendency to allow for building point solutions before deciding whether to generalize a given use case into a generalized platform capability. Can you give some examples of cases where a point solution remains a one-off versus when it needs to be expanded into a widely used component?
How do you identify and tracking cost factors in the data platform?
What do you do with that information?
What is your approach for identifying and measuring useful OKRs (Objectives and Key Results)?
How do you quantify potentially subjective metrics such as reliability and quality?
How have you designed the organizational structure for your data teams?
What are the responsibilities and organizational interfaces for data engineers within the company?
How have the organizational structures/patterns shifted or changed at different levels of scale/maturity for the business?
What are some of the most interesting, useful, unexpected, or challenging lessons that you have learned during your time as a data professional at DoorDash?
What are some of the upcoming projects or changes that you anticipate in the near to medium future?
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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