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Вміст надано Felipe Flores. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Felipe Flores або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.
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#158 Part 1- How do we get data scientists going in 8 hours with Nitish Mathew Global Head of Data Engineering at Afterpay

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Вміст надано Felipe Flores. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Felipe Flores або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.

When Global Head of Engineering, Nitish Mathew looks back at his journey over the last four years with Afterpay, he describes it as a phenomenal ride and a tremendous learning journey. They have had to reinvent themselves three times in a very real way, getting rid of their first and second platform and building with the view of not assuming that this is for the next 10 years, but building with the view that they will need to get rid of this in two to three years.

Nitish says when a data scientist joins a company they should be able to query a data lake or data warehouse like any library. We need to help users find information quickly so they can actually get the information that they want to do their research immediately. It needs to be organised so people know how to find what they need.

We end part one with a discussion on how organisations should use coaching to get the best results. Ultimately, leadership's role is to get the right outcomes and support people with coaching, technology, decision making and resources. He believes the goal is not to build the fastest product. The goal is to make sure your data scientist colleagues are happy. And for them to be happy, you need to make sure that she or he is able to do their job fast, which involves giving proper easy to use performance tooling, giving good data, and then making sure that on a daily basis, it works.

Enjoy the episode!

Read the full episode summary here: Ep 158

Thanks to our sponsor Talent Insights Group!

--- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message
  continue reading

268 епізодів

Artwork
iconПоширити
 
Manage episode 295070468 series 2310475
Вміст надано Felipe Flores. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Felipe Flores або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.

When Global Head of Engineering, Nitish Mathew looks back at his journey over the last four years with Afterpay, he describes it as a phenomenal ride and a tremendous learning journey. They have had to reinvent themselves three times in a very real way, getting rid of their first and second platform and building with the view of not assuming that this is for the next 10 years, but building with the view that they will need to get rid of this in two to three years.

Nitish says when a data scientist joins a company they should be able to query a data lake or data warehouse like any library. We need to help users find information quickly so they can actually get the information that they want to do their research immediately. It needs to be organised so people know how to find what they need.

We end part one with a discussion on how organisations should use coaching to get the best results. Ultimately, leadership's role is to get the right outcomes and support people with coaching, technology, decision making and resources. He believes the goal is not to build the fastest product. The goal is to make sure your data scientist colleagues are happy. And for them to be happy, you need to make sure that she or he is able to do their job fast, which involves giving proper easy to use performance tooling, giving good data, and then making sure that on a daily basis, it works.

Enjoy the episode!

Read the full episode summary here: Ep 158

Thanks to our sponsor Talent Insights Group!

--- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message
  continue reading

268 епізодів

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