The four levels of data engineering!

[The-KP]

Level 4 is really an attribute of Level 1, 2 and 3. If you're not communicating with stakeholders, even when just being assigned a ticket, you're not being a team player.

[fantsepants]

How does one practice beyond level 1? It feels impossible to get beyond level 1 unless you're a level 1 in a company that also deals beyond that.

[deafmute]

Communication is tough. I had a stakeholder wanting me to explain a couple of days ago what was going on with a data issue by using train cars and passengers analogies.

[dereko]

I’m a bachelor learning SQL. I want to date-a-model.

[shubhanjandash]

Hey dude, been following you since a long time on LinkedIn. Glad you're making video content for us. Bless you!

[chrisgarty]

Did not see that plot twist coming: wizard level 4 is communication 😆

[artofkeisha]

I do like this content and I will be following for more! 👌🏾

[supercompooper]

My data modeling should appear in the cover of Vogue magazine.

[SM-vzek]

Data modeling was all fun and games until I discovered your free bootcamp 😂.. thank you very much 🎉

[bouzie]

I’m a software engineer but I’m tryna get like you. I love data so much

[Alex_]

Can you give some pointers when moving from lvl1 to lvl2? I am self-employed. Love the content 👍

[primekrish]

Very well said, talk to stakeholders before building anything should be the no.1 job of any engieer.

[dennisirorere]

The scope of data engineering is much broader than the categories you've mentioned.

There are different types of data engineering: business-facing and platform-facing.

Business-facing data engineers interact with stakeholders, gather requirements, and focus on how data can drive impact within the organization.

Platform-facing data engineers handle systems, source data from various external sources, and ensure that business data engineers have the data they need every day.

Some organizations also have a data enablement team that provides data tools across the company.

While technical depth varies, it's not a one-size-fits-all situation.

Great video, by the way.

[Sindoku]

There is also me. a senior dev with mostly FED experience, who knows the basics of BE application dev (Node/Python/Go), but wont risk swapping to a BE job because I’m worried that I’ll suck at data engineering and will end up running sub optimal queries along with many other mistakes. I can definitely handle API creation and SQL/No-SQL as long as the ORM takes care of optimizing the more advanced queries for me, but to me, a decent BE dev is at least level 2 with data engineering, granted in reality I think most BE devs are actually level 1.

[Geoff_the_Chum]

Unknowningly at level 2 because my job forces me acquire skills to keep up😂 now how do I learn more about.

[Shwill]

Hey Zach, hopping to get your input here - so I know both SQL and Python. My team does all their modeling with PySpark. I use both (mainly SQL tho), SQL for data transformations and PySpark for only cleaning up unstructured data and writing to storage accounts. Our only job is to model / create tables for analyst and the business to use. They’re pressuring me to only do PySpark because they don’t understand SQL well, I feel like they have it all backwards and just more so don’t want to learn SQL. What do you think? Am I in the wrong here?

[zahidc]

Thanks, this was useful in gauging my own abilities.

[oriarsenal]

Lmao level 4 was gold. I’m a data analyst myself and that actually made me laugh out loud

[samsspam]

I’m proud of myself. I know some of these words!

[johnnycastillo]

What do you recommend to learn distributed compute?