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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?
- What are the primary sources of data that you collect?
- 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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