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This is another post in our series written by alums of Analytics Engineers Club. We’ve invited brittany bennett as a guest to write about how AEC shaped her journey as a data leader.

My journey through the world of data

I got my start in data in 2020 when I became the Data Director for a large, national youth climate protest group. I was brought on as their inaugural Data Director to engineer their data infrastructure from the ground up, develop the organization’s data strategy, and uncover insights that would build the people power to pass bold climate policy. This was my dream job.

But, like so many of my peers in progressive politics, I walked into my new job with minimal formal training in data. Many people in political data get their start in organizing. A common career path for a person in my field is to work on a campaign, assume the role of an admin of a campaign’s political data software (most often VAN), and then pick up some Google Sheet magic to build custom reports for your field program. This is a viable career path for political data practitioners, as it exposes you to the skills needed most to succeed in this field: spreadsheets, VAN, and deep domain expertise in organizing.  

My career path was not so different. I have a degree in civil engineering, but quickly pivoted into political nonprofit work after graduating college. In one of my roles as a fundraiser for a youth vote advocacy nonprofit, I taught myself data analytics in order to analyze our monthly giving program and unearth new strategies for optimizing revenue. By the time I landed the job as a Data Director, I had deep knowledge of organizing field programs, exposure to political tooling, and enough knowledge of Google Sheets to be dangerous. I was a prime candidate to step into the world of political data, but I had big shoes to fill if I were to truly lead data analytics for a national organization. 

Trouble in paradise 

During those first few months as a new Data Director, I floundered. I was thrown into a data warehouse with hardly any onboarding and asked to crank out dashboards. I wound up building these overly complex dashboards where every chart was supported by a 200-300 line SQL query. If we needed to tweak the logic of the dashboard, I would go back into each individual chart and manually edit the SQL, which in turn rifled my dashboards with tiny errors. I found myself writing the same SQL over and over again–pulling demographics on participants for various events, reporting on our active membership, and pivoting survey-form responses from our CRM–and asking myself if there was a better way.

It turns out my problems were not unique. I started searching the Internet for solutions to my problem: how can I transform my mountains of messy data into something of value to my organizers and stay sane in the process? In my quest to uncover better data practices, I discovered the quintessential article by Claire Carroll that outlines a whole new discipline for people like me: analytics engineering.

In the article, Clarie educates the audience on an emerging field of data called analytics engineering. If you have ever wondered “what is analytics engineering,” Claire addresses the confusion directly in the first sentence of her article, declaring that “analytics engineers provide clean data sets to end users, modeling data in a way that empowers end users to answer their own questions.”

I was immediately hooked. My job was to deliver clean data to end users, and instead of marketers or product managers, my end users were organizers. The concept of analytics engineering had been absent from my trainings and community in political data. I knew that my job as a Data Director was first and foremost an analytics engineering job, and that many people in politics were also analytics engineers, even if they did not know it. 

Discovering Analytics Engineers Club

As my luck would have it, Claire Carroll, along with another data legend Michael Kaminsky, had recently launched a novel training to transform analysts into analytics engineers. Analytics Engineers Club (AEC) immediately caught my attention because it offered the skills I needed to be successful in my job, but were not being taught anywhere else (at least, not within the progressive politics space). 

I took the AEC course in 2021, back when there was still a live cohort. It is no exaggeration that AEC changed my professional life. Before AEC, I was clueless about the inner workings of the data warehouse I used every day. My idea of writing Python involved cracking open a Jupyter Notebook–I had never even heard of a Python debugger. I thought linting had something to do with dust. I’d shrink with embarrassment any time someone asked me to perform an operation with my terminal, knowing it was a skill I needed to be a strong programmer and not one I had. 

AEC enabled me to unravel a mess of rambling SQL code and turn it into a functioning, robust dbt project. I learned the importance of building tests on top of your code, and by implementing these practices I was soon able to catch bugs in my code well before my organizers. My technical skills grew beyond SQL, and I was able to use my new Python skills to build ELT pipelines for my organization to bring in more data sources to our analytics pipelines. With substantial help from Claire herself, I was able to automate all that repeated SQL with Jinja, freeing up hours of work a week from my team. Not only did my performance improve at work, but my confidence did as well. I had the skills I needed to do the work, and I had the framework of analytics engineering to continue deepening those skills. 

Spreading the gospel of analytics engineering 

I sent everyone I managed through AEC. I wanted a world-class data team, and I knew AEC would get me there. With my full team leveled up as analytics engineers we were able to dig ourselves out of our tech and start proactively innovating on solutions for our organizers. We built out a robust, well-documented dbt project that synthesized data from nearly a dozen sources, cleaned millions of rows of messy organizing data, and delivered accurate metrics on the growth and diversity of our membership to our organization’s top leaders. We earned a name for ourselves among other progressive data professionals for adopting modern data stack best practices from tech, and for proving that those best practices worked. 

I want what AEC did for my team for every data team in progressive politics. I want a world where data staffers are nimble with the command line, understand how dbt could transform their work, are aware of the magic of Redshift that lurked underneath the veil of Civis, and are capable of engineering solid pipelines with a scripting language. These skills are not commonly acquired for many of us who wind up in political data, but AEC presents an opportunity to learn them. If you work in political data, or resonate with my anecdotes of wallowing in massive amounts of messy data, then I encourage you to sign up for Analytics Engineers Club and master the art of analytics engineering.

You can read more about my journey into the world of political data on my website at