Catching Up!

Hi Internet! It’s me, Simon! It’s been a while!

I won’t do the thing where I apologize and explain the slow post rate for the last year or so – you’ve been here on Earth, you know what’s been going on. Kids & COVID are a tough combo!

One big change I do want to talk about is that I have a new job! If you’re reading this, it is super likely you know this already, but last year in August, I left Automattic, where I’d grown a TON as a person and professional, and have nothing but gratitude for my time there – to join Fishtown Analytics, a smaller software startup working on the modern data stack, with a tool that I saw as a world changer.

To be clear, I still think it’s a world changer! It’s dbt – Data Build Tool – and while it transforms data, what it really does, at a strategic level, is transform organizations. It reminds me a lot of WordPress, in that it works, on a fundamental level, to take an area of work, a task, a job to be done, and pulls it from a rarefied place, accessible only to a few scarce specialists, and democratizes it, makes it a tool of the people, for their many and varied ends, and with the benefit of their other personal and professional specializations.

For WordPress, it was websites, for dbt, it’s the lifeblood of the modern organization: information and the translation of information into insights – what an opportunity!

I also made a bit of a surprising career move, to some, in that I stepped from a Director level position in the data organization at a stable, established company where I had significant organizational and personal capital (as well as a lot of close friends and mentors!) , to join a startup with less than one fortieth the headcount (my team at Automattic was about the size of Fishtown’s entire product engineering organization at that time!) – in the midst of a global pandemic, with two kids at home (literally at home!) – to an individual contributor role in the Product space, their first external Product hire, in fact.

I’m so glad I did – there are times that I miss the folks I worked with at Automattic, but the energy and dynamism of a small, scrappy company, it really is different, and I think it really does suit my temperament.

I have two things on my mind, probably they deserve full posts of their own at some point, but, just a quick stab at them for now – a couple things that have been sitting heavily on my mind:

Running an Analytics Org is a lot like Product Management

(there are also a lot of ways that it is nothing like product management but that’s a different topic)

It turns out, reporting, data pipelines, robust testing, and data products, these things add up to something like a Real Product with Real Customers, and a lot of the skills you learn building a successful internal analytics practice can really serve you well in a product capacity. The other side of that, of course, is that if you’re working in the analytics space, I super strongly recommend learning the basics of product management – it’s a toolkit I wish I had more explicitly pursued earlier in my career!

A shout out here to Emilie and Taylor, who gave a great talk about this at Coalesce:

Analytics Engineering Isn’t Going Anywhere

This part is just an observation: Analytics Engineer as a job title, and the skillset of the modern data stack, simply isn’t going anywhere – it’s growing faster than anyone can keep up (take a look at how many job descriptions there are looking for dbt practitioners in any professional data community!) – there is still so much room for this new slice of the market to grow, and I strongly encourage folks thinking about a career change to seriously consider the space!

Here’s a great intro to get you started.

The Emergent Import of the Tangible for the Remote Workplace

(what a title!)

The longer I lead remote teams, the more clear it becomes to me, that there are things which take on an outsized importance within a remote team – especially within an all-remote organization.

(an aside: I appreciate the definitional challenge of calling an organization “all remote” – remote from what exactly? “Distributed” is an alternate term that I appreciate more but, has yet to gain a ton of traction)

Some of the things that take on an outsized, and sometimes surprising, importance, that I’ve written about in the past:

Phatic Communication

Eating On Camera

What’s starting to appear more and more to me these days, is how important, or perhaps how impactful, the tangible becomes.

What do I mean when I say, the tangible?

Over the course of the last year, we’ve built out an in-house ETL solution that we call SQLT – pronounced “Sequel Tee” – and had to work really hard to gain buy-in from folks within the organization to start using it, rather than defaulting to other solutions that were more familiar or more comfortable, for whatever reason.

Once we had a couple dozen folks who had committed code using SQLT, I had some stickers made, and I mailed them to those who had a commit on their record – they were a surprise, no one was aware of them coming. I trucked down to the post office and sent a dozen envelopes to three different continents.

I have a few left! They look like this:

sqlt.jpg

…and it was a great hit! Folks really appreciated them!

I wonder, if we were in a co-located environment, if it would have had much of an impact. Since, in an in-person workplace, you have many, nearly constant, tangible artifacts of your work – an office, a desk, a cafeteria.

In a distributed workplace, the tangible is much more rare: your workplace is your home, or a cafe, or another shared space – areas that aren’t exclusively the place for work, and often serve many duties.

You don’t often interact with your colleagues in the flesh: this is part of what makes regular meetups or conferences an important source of connection and re-connection.

So too, I think, receiving something you can hold, something that comes in the mail, from your otherwise largely ephemeral colleagues, takes on an outsized impact in a distributed environment.

I used to send my teammates postcards on their birthdays and their Automattic hiring anniversaries (naturally, Automattiversaries) – something that would probably seem a bit odd in a co-located workplace, but in a distributed one, it really felt like a special token of recognition, a tangible touchstone of the time and the work we’d done together.

Due to the intentionality that phatic communication requires when working remotely, it’s easy for distributed workers to fall into communication patterns that are totally professional: interactions can be purely work focused, transactional, without the kind of socially pleasant borders and decoration that you get for free in a co-located environment.

Asking your colleagues to eat lunch on camera can feel a bit awkward or out of place.  After all, in a co-located environment, which we still have in our brains as the default, it would be odd to ask for! But, part of the distributed organization’s success relies upon us recognizing the things we no longer get for free, what we maybe took for granted in a co-located office, and how we might replace them, or improve upon them.

Like that eating-on-camera piece, I think a birthday or work anniversary postcard would be strange in a traditional office – but it is, not only not strange, but maybe quite important in a distributed workplace.

The injections of the tangible help remind us that our colleagues and relationships are real – a postcard or a goofy sticker, by existing between our fingers, offers a kind of reminder that our colleagues too, are real and tangible.

 

 

Become an Analytics Engineer!

OK, so let’s get something out of the way up front – yes, I wanted to be a data scientist.

But you know what? Once, I also wanted to be a professional coffee roaster.

These jobs (and aspirations) are similar primarily insofar as that my desire to have them took a nosedive once I got a real glimpse of what doing them was like.

If you like the idea of working with data, if you see yourself as someone who has ambitions or aspirations working in the data space, you should read this article from Dan Friedman – Data Science: Reality Doesn’t Meet Expectations

I work closely with data scientists – in some ways I genuinely envy their approach to work, and the way that they can find impact within organizations. I am super glad they are out there and I am so grateful for the insights and thoughtfulness they bring to the table – but that job’s not for me!

The job that I’ve found suits my nature, allows me to have a lot of impact, and work on important and interesting problems, is a new one – the Analytics Engineer!

Job titles in data and in tech are hard – do we really need a new one? The Analytics Engineer is this sort of emergent term, that describes an area of work that folks have been operating in for a while now, but with modern tooling and third party solutions has seen a rising need.

NB, not everyone knows that they need an Analytics Engineer – often you’ll see job descriptions for titles like Data Analysts, Business Analysts, Data Engineers, even Data Scientists – but the work that will be expected is Analytics Engineering work.

That work is more technical than a strictly Excel based analyst – no disrespect to Excel, sufficiently advanced Excel is indistinguishable from software engineering in my opinion, but, you will need some SQL chops to be effective as an Analytics Engineer. It’s less statistically heavy than a data science role. It requires literacy in data engineering but, in most cases, not necessarily the chops to originate an Airflow DAG. Strong opinions about data architecture is helpful but, often you can learn that on the job!

As I talk more with folks about this kind of work, and as we struggle to find qualified candidates for our own teams, I realized that I’ve repeated the same advice probably a half dozen times: sometimes to friends, at least once to an Uber driver, over Slack and in person. When this happens, I take it as a strong signal that I ought to put up a blog post!

So here it is: this is my guide to how you can become a competitive candidate for Analytics Engineering roles (even if they’re hiring for the wrong job title!)

One of the challenges to gaining the kind of experience you need in order to become a competitive candidate is that much of the best in class tooling for this kind of work is either hard to use alone or prohibitively expensive – something like Airflow is a great solution and very broadly used, but, it’s going to be a challenge to set up locally to use with toy data. Looker is a very common tool for this kind of work, but is terribly expensive for an individual to use as an educational tool.

So, this set of suggestions is meant to be used in reality by anyone – you should be able to follow this advice at low or no cost.

Yes, if a job description is looking for Airflow ETL experience or Looker modeling experience, you won’t have exactly that – BUT as someone hiring into a role with exactly that wording in our job description, I also recognize that the free tooling below is eminently transferable to the tooling that we use in-house. You can mention that you accomplished the same tasks with a different tool and that the skills are laterally transferable in the cover letter – a cover letter with that kind of attention to detail is already ahead of the pack.

Here’s your stack:

FIRST you have to find some free data that you’re interested in. That second part should not be neglected – if you want to see this project through to its completion (and gain your Competitive Candidate merit badge!) , it is absolutely imperative that you make choices that make it as easy as possible for you to stay motivated!

Are you interested in food? See if you can get data from your local agricultural co ops or agencies on historical data. I’m interested in local politics, so I FOIA requested the voter registration data for the entire State of New York – it came on a CD!

NYS_CD

Being interested in the data you’re using is going to make a big difference when it comes to understanding it, modeling it, and then building some reporting – especially if the only end consumer is you! Bonus points if it is a streaming source of regularly-updated data, like web traffic or an ecommerce application.

SECOND I recommend using BigQuery as your data storage solution – they have good docs, they have a free plan, and they integrate really easily with the other parts of the data stack. If you have another solution you prefer, that’s fine too!

THIRD You must learn the excellent and open source dbt from your friends and mine at Fishtown. Here’s the tutorial and here is the Slack community. dbt is what you’ll use to take your ocean of raw data, transform it into  tables that fit the dimensional modeling standard, and apply robust testing to those transformations.

If you have a little extra cash for this endeavor, I recommend buying the Database Warehouse Toolkit and reading the first four chapters to really dig deep into dimensional modeling. If you’re trying to stay absolutely no-cost, you can suss out some blog posts and other resources for free!

FOURTH You’ll build out your final reporting using the free tier of Mode Analytics – note that in order to stay within their free tier, you may need to reduce your final reporting tables to “Thousands of Rows” – take this as an extra challenge to your transformation later, and an opportunity to additionally leverage the power of dbt!

FIFTH Make sure you document the journey – I always recommend blog posts, but probably a well documented Github repo will be more interesting, and more likely to be reviewed, by most technical hiring managers.

At the end, your process would look something like this:

I recognize that the above glosses over a lot of the work that is behind this proposal – probably a dedicated person already working full time, putting in some time nights and weekends, could get through the above in six months. It’s not a short trip, but, if you’re looking to make a move, this is one way to do it.

The need for Analytics Engineers is only growing, even if the job title itself is still only starting to gain steam – I hope you’ll give it a try!

I’m Giving Video Content a Try!

As y’all may recall, last year I was lucky enough to spens some time working with the fine folks at Locally Optimistic to produce and run some AMA content for them – they ended up being more similar to traditional interviews, but folks seemed to enjoy them!

You can find those all here!

These were well received, and generated a TON of insight for folks working in the data and analytics space – but I had a few things I wanted to try doing a little differently:

  • They could be more discoverable: it was tough to know which guests talked about what, they were about an hour long so it was a big bite of content if there was only one thing the viewer was interested in – even with YouTube’s search function it’s likely folks were leaving before the parts they were interested in arrived.
  • They needed a little more social support: I tweeted about each one, but probably different parts and points of the conversation could have warranted its own outreach.
  • The live format, where we’d schedule them and invite members of the community to join, and then post afterward, was a bit tough to schedule, and we never really got the community engagement during the calls that we had hoped for.

So, I’m putting together some videos that hopefully are a step in the right direction – I’ll chat with similar folks, luminaries in the data and analytics space, and then publish the entire conversation, but also smaller chunks (ideally one per topic) which can be posted separately so that folks who are only interested in, say, data career ladders, can easily find and watch only that piece.

I still absolutely have a lot to learn – both about being a data professional as well as producing and sharing video content! – but, I’m giving it a try! I’m also hoping to use this energy to help carry me into blogging more, once more – but that’s a perennial hope, isn’t it?

With no further ado, here is the first full-length conversation, with my friends Stephen and Emilie – I think you’re going to like it!