What was your career path into data?

Sent 13 Apr 2021

Hi friends,

What was your career path into data? This is a question we’ve been asking a lot of people lately, and the answers have been really fun! On our team alone, we have scientists, engineers, philosophers, and musicians. Often driven by curiosity, these people learned enough SQL to be dangerous, and ended up finding their way onto a data team.

There’s so much to love about having these diverse career backgrounds on one team, but I’ve recently been wondering why this is the case! Some working theories:

Data is a newish field, and college curricula haven’t caught up — as such there is no traditional vocational path onto a data team (say, in the same way that a civil engineer had to study civil engineering). There are some masters degrees, but they often aren't teaching relevant things.
It’s hard to learn how to work with data until someone gives you a set of database credentials, and therefore adjacent roles (ops, customer support, finance) end up being funnels into a data team
People that aren't on data teams don't really know what exactly it is that data teams do. This is further conflated by the ambiguity around titles on a data team: one team’s data scientist, might be another team’s data analyst, while one team’s data engineer, might be another’s analytics engineer.
All of the above? Other things?

I’m curious:

What was your career path into data?
How did you end up learning the skills required on a data team? Are there any resources you’d recommend?
How does the thing you were doing before being on a data team make you a better practitioner?

I might do a roundup in the next newsletter! — Claire, dbt Community Manager

From the dbt community #

Questions, ideas, articles, and useful insights from our community. Many of these discussions take part in the dbt Slack group — you can sign up here.

Should your team have one BI tool, or multiple (with each solving a particular use-case)? There was some really great conversation about this topic in our new #bi-tools-general channel.
Should everything live in dbt, or should some logic live in your BI tool? A few people weighed in on this thread. (Answer: mostly in dbt!)
A few different threads on “Reverse ETL” lately (someone even tried to blame credit me for the “Reverse ETL” name 😬) —why is it called Reverse ETL, and does it replace Fivetran/Stitch? And a writeup from Adam Stone on how Netlify uses "Reverse ETL" in their stack.

A few things you may have missed from us #

dbt 0.19.1 was released last week! This is a performance release, with a focus on faster project parsing: ~3x faster, on average. That means less time waiting between typing dbt run and seeing your first model hit the database. Everyone should upgrade, but if your project is on the larger side, it’s probably worth doing it soon! Release notes here.
We published our first episode of dbt-tv (dbtv?). Catch the 8 minutes of goodness (or 5 minutes if you watch at 1.5x speed) here
I wrote a short article about a technique I use to break down seemingly-complex modeling problems into smaller pieces (what’s the point of having a newsletter if you’re not going to plug your own work 👀). The resulting thread was even better than the article!

Great companies currently hiring #

Data Engineer at JetBlue (NYC) 🛫: The team at JetBlue are one of the most advanced teams using dbt, and are blazing the trail on what it means to use dbt at large companies. Want to get to know the team before you apply? Their Manager of Data Engineering, Ashley, gave two incredible talks at Coalesce— one on how JetBlue migrated to dbt, and another on how they secure their PII.
Data Analyst at Aula (remote between London and Chicago) 🏫: This startup is doing some really cool things in the edtech space, and is working with our favorite data stack! I really loved this recent article from their Data Lead Kelly Burdine, that debunked the idea that you have to spend 80% of your time cleaning data (hint: use dbt, and then you don’t have to keep re-cleaning!)
Senior Analyst at Grailed (NYC) 🛍: My coworker Grant has been working with the Grailed team, so I asked for his input: “Bilal and Seth, Director of Analytics and Senior Data Engineer respectively, have been building a best-in-class dbt-fueled view on online marketplaces for a few years now, and are a genuine delight to work with. If you're passionate about weaving together events, marketing attribution, and customer behavior into compelling narratives, and doing so within a thriving two-sided marketplace, I can't imagine a better role.”
A couple of sidestep roles that I think are worth including in case someone is looking for a slight change in their role — product advocate at Firebolt.io (a new data warehouse), Head of Community at Snowplow, and a part time technical writer / developer advocate at PopSQL

Check out the #jobs channel in dbt Slack for more listings (or to add one yourself!). I also heard a rumor that there might be a jobs board, with filters for location and role, coming 🔜.

Upcoming events #

Here’s where community members will be speaking, hosting, or attending. If you have an event to add to list, just reply to this email with the details:

April 13: NYC dbt Meetup: I loved the Feb edition of this meetup, hosted by our friends, Brooklyn Data Co (you can catch it here). There’s another one coming up next week, hope to see you there! (And by there, I mean, on Zoom, of course)

April 29: London dbt Meetup: We’re hopping across the pond for a meetup in London — we’re still looking to fill a speaker spot, so if you’re UK (or EU) based, and you’re interested in giving a talk, reply to this email to let me know!

May 13: Staging: dbt Demo Day: These are quarterly events that bring our community members into the dbt product development lifecycle! We’ll share what our product team has been working on, and hear how some of our community members are using these features in their dbt projects.

dbt Learn: We’ve got some courses coming up which are other-side-of-the-world friendly! Check them out here.