Adam Stone is currently an analytics engineer on Netlify’s data team. Thanks Adam for sharing your story!
TL;DR: Analytics engineering wasn’t the obvious choice for Adam; he came to it through a bit of a windy path, like so many of us in this field. Here’s a quick run-down of his career flow:
Take it away Adam!
Before I started using dbt, I was doing a PhD in neuroscience. But after I finished, I decided that I wanted a tech career instead of continuing in academia.
I was the first data hire at Convo, a telecom in Texas, and I was a data team of one.
The CTO came up to me my first week and said, “Adam, we need a data warehouse. Please do that.” And I thought to myself, what even is a data warehouse? What is that? I had been working with academia datasets (mainly CSVs) and this was totally new.
So I took a few months and researched everything I could find, anything on the internet. I happened upon dbt, but that was back in 2018, and dbt wasn’t as big as it is now.
I thought, “Yeah, I’m really lucky to find this!” I found the demo video by Drew, watched that, decided dbt was definitely the right fit for our company and for our team. So I joined the Slack community and started implementing dbt.
First I had to build a demo of the data stack for my CTO so I had to set up and figure out Snowflake and dbt and Mode, our BI tool.
And, I was also learning SQL at the same time — I had been using R prior to that, but dbt and the rest of the data stack was centered around using SQL.
But the dbt demo video was really helpful. I’m sure I asked a hundred questions in the Slack community in the first month while I was getting everything set up. For example, what was Jinja for and how could I use it? I was asking those type of beginner questions.
What I remember the most is that so many people answered my questions and helped me out. And very quickly I was in a position to also help other people and answer their questions. So I got a lot from the dbt community and I was able to give back as well.
I was a data team of one for about a year and a half, and I was doing both of what I would call data science work and analytics engineering work. And I was torn between the two when thinking about my career path.
I liked the idea of remaining a data scientist – I had a neuroscience background after all, and this was my job title at Convo – but the work of analytics engineering felt more interesting to me.
But I decided to stick with data science, and joined Deliveroo on their data science team. There, I definitely was doing more data science-y work: planning experiments, advising on business strategy, reporting on metrics.
After a year in that role, I realized that I preferred more analytics engineering work–building out things, organizing data, thinking about infrastructure. I found myself increasingly seeking out that type of work.
So that’s what I’m doing now at Netlify.
During the onboarding process at Netlify, I saw a talk by our manager Emilie [Schario] that discussed two different types of work that happens in the data analytics space.
There’s circular work, where you’re looking at analyzing things, showing it to stakeholders, and answering questions. And, you know, you get that kind of feedback loop where they ask more questions, you go back, you do more research, you’d come back to stakeholders and do it all again.
That’s compared to linear work where you’ve got an issue, you engineer a solution, build it and you close out that issue, and pick up on a new issue.
I don’t think that there are strictly “two types of people,” but people may prefer one type of work over the other. I’m definitely more of a linear work-type of person, and I think with analytics engineering you encounter more of that type of linear work.
What is an analytics engineer? A librarian, it’s a lot like being a data librarian.
So, if you are running a library, you have these books coming in and you have people who are looking for books on specific topics. You’ve got to figure out a way to organize all those books so that all those visitors can find what they need.
But there are many different ways to organize books–there’s not just one perfect solution that fits everybody. You’re going to need to use an organization system that works for the visitors you’re working with.
So I think that a librarian is interested in helping people find the books that they’re looking for, but also to help them discover new books that they didn’t realize they were looking for.
For example, if you’re looking for a cookbook, the librarian will tell you which aisle to go and you know, where you can find all of the cookbooks that are in that aisle.
But then maybe because I’m in that aisle. I see next to that, that section of books is, you know, mechanics books, or maybe on the other side there’s books about knitting.
So the kind of discovery that happens in a library is the way data discovery happens as well. A well-organized library can help people find data but also discover new data.
I’ve worked in data now for eight or nine years across academia and the tech world, and trust is one thing that you cannot play around with.
You get a lot of it coming into any new work situation and, you know, you can lose it quickly and then it’s hard to get it back.
But, I believe that as long as you’re transparent about what’s happening, if you explain what you’re working on and the things that you can’t work on because you’re working on other priorities, then you can absolutely maintain a high level of trust not only with your stakeholders but also with your own team.
That’s one of the really cool things about the dbt Slack community, because I got the job at Netlify through this community!
We weren’t using dbt on my team at Deliveroo, but I was still a part of dbt Slack and the community because it’s also just a fun place to be! I shifted into a lurker role and you know, had just kind of been quietly observing as topics and discussions came up.
And then someone asked a question that I knew the answer to, even though I hadn’t been using dbt for a while. So I answered that question and Emilie [Schario] popped up and said, “Haven’t seen you on Slack in a while! It was nice to see your name today”
And I responded, “Great to see your name too!” and then I remembered she had posted a couple openings on her data team recently. I asked if she was still looking and she said yes!
If I hadn’t stayed a part of dbt Slack, this wouldn’t have happened, and I probably wouldn’t have even known about the open roles at Netlify.
So, yes, the Slack community is how I ended up at Netlify. It’s a great space for serendipity!
The interview process was similar to a lot of other tech companies: First, you do a phone screening, then you interview with a manager, then you get a take home project, then you interview with members of the team and with stakeholders.
But what I liked about Netlify’s process is that it was very transparent, very clearly communicated as far as the steps.
They had a cool web app, which would show the interview timeline with icons that would tell you, “Okay, you’re done with this step, now you’re in this stage of the interview process.” It was really clear and made me feel like I was in good hands with people who were taking care of me throughout the interview process and the interview itself.
My interviews at Netlify were one of these situations where I felt an immediate connection with Emilie and with the rest of the data team. I could see myself working there very easily. The problems that they were discussing were problems that I was familiar with. I could right away envision specific solutions that would address their problems very well.
In the end, I believed we would mesh well, and it’s really a special thing when that happens during an interview process. I’m definitely happy being at Netlify and working with Emilie.
I’ve been there for three weeks now, and what I’ve enjoyed the most is just onboarding and getting to know people. I’ve already merged a few PRs.
I really enjoy working and talking with my data team. Many of us are new to Netlify–we’re virtually a new data team. We’ve all been hired within the last six months. So we’re figuring out all of this data stuff together–what does Netlify need now?–and we’re having fun while we’re doing it.
At this stage, because we’ve got a new data team and dbt was just implemented in the past six months, we’re still putting a lot of basics in place right now. It’s an exciting stage to be at, because we’re deciding all of the conventions, the structure, everything. Many of the decisions we are making now will set the tone for Netlify’s data work over the next three to five years.
Of course, three years out, things probably will get complicated. But Emilie has shown us a fabulous roadmap for the first year that outlines what we’ll be working on, building the foundation of our data strategy so that we can enable the other things that we want to do with our data. It also outlines what we won’t be working on, to set expectations for everyone else at Netlify.
And to make this possible, we first have to build out our infrastructure. That work is a lot of what analytics engineering is all about–it’s like setting the table.
I went through moments of uncertainty about my career, fretting over data science or analytics engineering. You’ll have moments where you need to make a decision about which career path you’re on and where you want to aim towards. And now I feel more confident that analytics engineering is “my thing” and this is my career path.
Analytics engineering didn’t even exist as an actual job title three years ago. Now there’s a huge community of us, but what will our community look like in five or 10 years? I’m excited to see this space evolve over time. How and where do analytics engineers advance in their career? Do we become head of data or principal engineers? As the analytics engineering community matures, we’ll be right in the thick of it figuring it out. I’m really excited to be here for that.