Building a data team is hard in any circumstance, but at a startup it can be even more challenging. The requirements are fluid, you probably don't have a lot of existing data talent to manage the hiring and onboarding, and there is a need to move fast. Ghalib Suleiman has been on both sides of this equation and joins the show to share his hard-won wisdom about how to start and grow a data team in the early days of company growth.
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack
- Your host is Tobias Macey and today I'm interviewing Ghalib Suleiman about challenges and strategies for building data teams in a startup
- How did you get involved in the area of data management?
- Can you start by sharing your conception of the responsibilities of a data team?
- What are some of the common fallacies that organizations fall prey to in their first efforts at building data capabilities?
- Have you found it more practical to hire outside talent to build out the first data systems, or grow that talent internally?
- What are some of the resources you have found most helpful in training/educating the early creators and consumers of data assets?
- When there is no internal data talent to assist with hiring, what are some of the problems that manifest in the hiring process?
- What are the concepts that the new hire needs to know?
- How much does the hiring manager/interviewer need to know about those concepts to evaluate skill?
- What are the most critical skills for a first hire to have to start generating valuable output?
- As a solo data person, what are the uphill battles that they need to be prepared for in the organization?
- What are the rabbit holes that they should beware of?
- What are some of the tactical
- What are the most interesting, innovative, or unexpected ways that you have seen initial data hires tackle startup challenges?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on starting and growing data teams?
- When is it more practical to outsource the data work?
- 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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email email@example.com) with your story.
- To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers