Before We Begin
If you haven't read the previous article in this series, I highly recommend checking it out here:
In that article I covered the sort of technology that Toronto hiring managers need from their candidates by surveying 120 different data engineering job postings.
That was a crucial step since, as I learned from another data engineering mentee, the German data engineering market has a very different set of regulatory requirements.
That means there is a demand for on-premise tech skills as opposed to cloud technologies like AWS, Azure, or GCP. If I didn't take that into consideration I could have potentially spent 12 weeks building a project with tech that isn't in demand!
So Where Do We Go From Here?
Picking a relevant data set. How do we know what is regarded as relevant? In a similar manner to the first blog post survey results, I will also summarize the top sectors in need of data engineers below.
The finance industry has the greatest need for data engineers followed by tech.
It is important to note that I categorized some job postings to have more than one sector category. For example, not all recruiting or consultancies served finance clients, but a majority of them certainly did!
Most sectors (not shown) only had 1 job posting. Avoid using autos, air line, compliance or gaming data sets if you want to increase the chances of creating a relevant project for Toronto.
Out of 120 unique job postings, 35 were finance related while 34 other sectors had fewer than 5 job posts.
In the next blog post I will detail my proposed pipeline setup and begin building it out.
You can find the previous blog post here:
And the next blog post here:
You can find my linked in here: https://www.linkedin.com/in/steven-aranibar-8891a2103/
And you can contact me at firstname.lastname@example.org