First time for everything. Welcome to the first of many blog posts. Expect candor, well-cited sources, lightbulb moments, and that feeling of saying what everyone else is thinking. Expect to learn something new about the tools we know and love. Expect to close this tab feeling a little better about what you can bring to the table in your own work. Expect hot takes on people management and the tech industry at large.

Hot takes such as… we are expected to downplay our work.

A handful of years ago, I was building and maintaining end-to-end data pipelines — using SQL to pull millions of data rows daily into a self-service analytics platform. I would then collaborate with ETL teams on the client side to ensure a smooth data flow, train end-users on platform usage, and consult executives on the platform’s effectiveness. I was an analyst working as a junior data engineer, consultant, and data quality professional. But, if you’d told me this in those words at the time I would have laughed and said you had the wrong Scar. Why?

Because no one told me! I was not told the work I did was equivalent to data engineering. I didn’t consider myself a consultant because my job title wasn’t Consultant.

I was an Analyst by title, but data analysis wasn’t a core tenant of my job. So, what was I? I didn’t think I was a Data Engineer, because I would have had that title. I certainly didn’t think I was a consultant because if I were, they would have given me the title, right?

When it was time for me to find my next data role, I didn’t have the confidence of a multi-accomplished data technician and consultant. Despite my Master’s in Information Systems and a few years already under my belt, I had been repeatedly told I didn’t have what it takes. If only I knew then what I know now.

Now, when I bring up to colleagues I have multi-industry experience in consulting, data engineering, and management — on top of data analytics — I’m met with a variety of responses. If they’re a true professional, they welcome my generalist repertoire with an open mind. On the other hand, if they have a limited view of how multifaceted our field truly is, they won’t believe me just because my current title is “Lead Data Analyst.”

While the latter view is so 2015, I understand where this misconception comes from. Aspiring data professionals are fed this narrative that you have to follow a linear trajectory dictated by arbitrary job titles to succeed.

If that’s true, then what does one do if their job title doesn’t match their workload? It doesn’t matter if you haven’t worked a job as a [Insert Desired Title Here] before. What matters is whether or not you have done the work, independent of the title.

Your career success is more than the sum of its parts. The problem is that those invested in our misfortune don't want us to calculate the sum of the parts.

When we calculate the sum of our accomplishments and showcase that math for the world to see, here’s how we need the rest of the industry to follow along:

Hiring managers need to seek out candidates who come from non-traditional backgrounds.
- If you’re an aspiring Data Analyst who moved over from teaching, you may be more comfortable breaking down complex concepts to diverse audiences, which is a core part of the job.
Companies need to pay workers for all the jobs they are working, rather than the 1 title they hold.
- You’d be surprised to know your market value likely far exceeds what you’re getting paid to do.
Our colleagues need to keep an open mind.
- This industry is not the same as it was 10 years ago. Or 5. Or 3. Between the middle of a global pandemic, the common-place insurgence of AI, and the permanency of remote work, we can expect just about everything we currently know about this industry to change. You never needed a college degree to land in data analytics. You never needed to climb the Data Analyst > Data Scientist > Data Engineer ladder. You likely already have what it takes.

You are more than the sum of what you’ve accomplished. The first step is to add up everything you’ve done.

Don’t Sell Yourself Short