Before dbt, it was hard for analysts to share work, and have visibility into what other analysts were working on. SQL jobs were scheduled through stored procedures or other methods, dependencies were not easily discoverable, and more engineering intervention was required. (View Highlight)
It was hard to know the real “source of truth”. You could have five analysts calculating “completed orders” five different ways. By using dbt and following best practices, we can eliminate a lot of these issues. (View Highlight)
Note: How do you move transition from legacy definitions to a consolidated definition?
While Data Analysts spend the majority of their time analyzing data, Analytics Engineers spend their time transforming, testing, deploying, and documenting data. Ultimately, Analytics Engineers are able to empower more users at the company in different functions to answer questions with data. (View Highlight)
“Hard skills”
Leverage SQL and git to effectively work and collaborate on a data team
Model data in a way to optimize for modularity and a single source of truth
Ability to visualize data in ways that make it more easily understandable and make time to insight faster
“Soft skills” / working with partner teams
Communicate, present data insights, and explain data complexities and nuances to a less technical audience.
Ability to speak with more technical teams like engineers, who may be in charge of data output from other systems that need to be ingested into the analytics data warehouse. (View Highlight)
