I saw a comment that caught my attention. The comment just mentioned, with an air of cynicism, that the latest trend in the business intelligence world was to change your job title to “Analytics Engineer”. The new buzzword of the data world.
It reminded me of the infamous memes with a caption that said “A data scientist is just a data analyst working in Silicon Valley”. It made me reflect on Analytics Engineering as a whole.
What is an Analytics Engineer?
By now, most of you are thinking: What exactly is an Analytics Engineer? It is a new role focused on bringing data engineering best practices to analytics workflows.
The ideal candidate for this role is a data professional who:
- Can develop data workflows from start to end – with little to no help.
- Is familiar with multiple programing languages and computer science concepts.
- Shows proficiency when it comes to writing and refactoring SQL and Python scripts.
- Excels at using the same tools that your analysts use (pun intended).
It is like hiring a data engineer, who has been in the shoes of your analysts. They know the ins-and-outs of using tools like Tableau, Power BI, or DBT (just to name a few); but they also have substantial knowledge of version control, documentation, automation, and testing.
But some Business Intelligence people do that already!
Some years back, you could also find data scientists with a background in computer sciences, who were exceptional at deploying models at scale. Nowadays, we just know them as Machine Learning Engineers and that is a great thing.
By creating a much better defined title, we are able to narrow these professionals’ scope. Ultimately, allowing them to do more of the work they do best!
Having a massive scope is a problem that should be familiar to every data professional. The world of business intelligence is full of different archetypes including:
- Data artists creating stunning visualizations and infographics.
- Architects fine-tuning servers and administrating databases.
- Communicators gathering requirements and teaching data literacy.
- Puzzle solvers who like to clean data and write difficult calculations.
I love the diversity, but I also think we are just making it more difficult for ourselves to manage expectations. We would be better off finding proper labels for each cluster of skills.
Sure, I enjoy building visualizations and I volunteer every time the sales team is looking for somebody to give a webinar. But deep inside, I am a puzzle solver. My passion is programmatic solutions and creating efficient data workflows.
To conclude this article
Analytics Engineering! Is it just an attempt to sound elitist? Is it just about differenciating yourself from all of the millions people, who learned to make dashboards and reports by skimming through a couple of Udemy courses? I don’t think so. There is a real need behind this new title.
If you read the description above and you feel that Analytics Engineer fits you better than your current work title, you might like to hear that we are hiring. But even if you are happy with your current job, let’s have a chat.
I would love to learn more about your daily tasks, the tools that you use, and the challenges that you face. And who knows? Maybe we will have our own conference once life goes back to normal.
