How to Land a Job in Progressive Data
Updated: Jul 6
This is a living document.
If you’re reading this blog, it is likely because you reached out to me asking for advice on how to get a job in progressive politics, and this is my advice to you. If you found this blog by some other means, well, I hope it helps.
So you want to work in progressive data. That’s wonderful!
I field a lot of requests from folk looking to start their career in progressive data. I cannot blame them: from the outside looking in not only does this field sound incredibly cool (a career that lets you leverage data for good?!) but it also looks opaque. Where would one even begin looking for a job in this field? What skills do you need to learn? What does it even look like to do progressive data?
I take on a lot of meetings with people asking these questions and more. And over the years I’ve sort of gotten into a groove of the advice I dole out. While every person I meet with has their own unique story, the basic building blocks of my advice remain the same. I now have my How To Land a Job in Progressive Data spiel committed to memory. So much so, that one day I sat down and in a frenzied writing spree put my entire spiel to paper.
This article contains the standard advice I normally give when people reach out to me for advice on how to land a job in this field.
I am **not** the expert here. I myself am new to progressive data and have a limited set of experience. I have been a fundraiser in a non-partisan youth vote group, briefly a researcher for a progressive think tank, a Data Director at an advocacy organization, and a Data Director at an electoral organization. I have never worked on the hard side (that is, directly on a campaign) and have the most experience with advocacy organizations. I have applied to jobs in progressive data, and I have applied to many more that I have not gotten (and learned from the experience).
The purpose of this article is not to prop myself up as The Voice on this subject, but rather to simply share what I know. Other people far more experienced and talented than me have written about this before, namely, the guide on Progressive Data Jobs linked here: https://www.guide.progressivedatajobs.org/
What is progressive data?
If you are reading this blog it’s likely because you somehow found out about the field of progressive data and know it exists. A lot of data folk who have the desire to Do Good wind up not knowing this field even exists! And there are reasons that political organizations, campaigns, and nonprofits aren’t, say, on college campuses recruiting or sharing their jobs on popular recruitment sites. Many of those reasons are Really Good reasons, but I won’t get into them here. Rather, the first step in this journey is just knowing that progressive data is a field that exists and that you can leverage data for good.
I didn’t discover this field until I was several years out of college and working as a fundraiser for a youth-vote group. Up until that point I had spent my entire adult life searching for My Thing. I had majored in civil engineering in college with the intention of leveraging what I called “my four-year bootcamp in technical problem” solving for good. That desire eventually led me to working in politics as a fundraiser, but I couldn’t shake the itch to get back into work that was a little more nerdy. One day at work I wound up meeting someone who held the title “Data Director” – but they didn’t work at a company. They worked at an organization that looked like mine. Something in my brain clicked and I dragged that Data Director out to coffee to bombard them with coffee. They were mostly uninterested in me, but over the course of the conversation I learned that 1) there was an entire ecosystem of folk like him that worked in progressive data and 2) that I could do it too.
When I talk to people about progressive data, I start by acknowledging just how big this field is. “Progressive data” is an umbrella term that covers a wide expanse of different kinds of places to work and roles to fill. I’m going to do my best to paint a picture of just how damn big this field is, but as I said earlier I’ve only been working in one corner of this field for a short period of time so my view is somewhat limited.
The work of electing candidates, advocating for policy, and unionizing workplaces spans across many kinds of entities. Here are a few of them:
Campaigns: Sometimes referred to as “the hard side”. These are the cycle-base entities that work to elect candidates directly. Campaigns range from small, local races to massive operations at the presidential level.
Advocacy organizations: Sometimes referred to as “the soft side”. These organizations are often nonprofits, which are legally coded at 501c3s or 501c4s. They often focus their efforts on advocating for issues such as climate change, abortion access, or racial justice; however, they also may engage in electoral work either directly or indirectly with candidates. You are likely familiar with a few of these. The ACLU, Sunrise Movement, and Indivisible all fit into this bucket.
Polling institutions: We love a good poll! There are some very smart people that conduct polling in this space. Folk that work here are often data scientists or otherwise have a strong technical and statistical background. Examples of polling institutions include YouGov/YouGov Blue and Data for Progress.
Unions: Unions powerful institutions on the left that support workers rights and organize for working class power. They can resemble advocacy organizations in structure and in data needs, but are in their own bucket. Examples include SEIU and AFL-CIO.
Research Institutions: The left also supports its own research operations. These folk study elections and other organizing to uncover tactics, strategies, and best practices for electing candidates and winning campaigns. Examples include Analyst Institute.
Vendors: It should come as no surprise that we have our own tools in progressive data. Believe it or not, we have tech companies that operate under the umbrella of progressive politics! These companies make the dialers, texting platforms, databases, canvassing apps, and more that campaigns and organizations use to conduct their work. If you are a software engineer or otherwise like working on tech teams, this is often the best place to look for a job. Examples include Strive, Scale to Win, Reach, Deck, and anything listed on the Higher Grounds Labs website.
In addition to types of entities that you can work at, there are also many, many different roles. We have data managers, data directors, analysts, data engineers, data associates, and data scientists, and that’s just a snippet of roles I could list off at the top of my head. There is no one “data job” in progressive politics. We are a vast and rich ecosystem and our job market reflects that myriad of needs spread out across the entities I listed above. If you like machine learning and statistics, there is a job for you. If you like writing reports and building dashboards, you’ll fit in just great. We have room for people at all levels doing all kinds of technical work.
It is worth saying, and repeating, that titles are borderline meaningless. A Data Manager in one organization could mean something completely different in another organization. A Data Director sounds like a Very Senior title, but can actually be a mid-level job in the right org or campaign (keep in mind my first real data job was working as a Data Director!).
So that begs the question. If there are these jobs, where do you find one?
Finding a job
Where do you go to find a job in progressive data?
In some ways, that is a relatively straightforward question to answer. We have our own jobs boards (again, there are reasons we advertise for our jobs within our own networks as opposed to posting on more common jobs boards). These are the sites you can check to find a job:
Progressive Data Jobs: The largest jobs board for progressive data. Run by amazing community members, with love.
All Hands: Upload your resume to a talent pool, and hiring managers from civic engagement, social impact, advocacy, and campaigning organizations across the US will reach out to you about open roles.
GAIN: Another jobs board for data jobs, and often includes the hard side/campaign jobs too.
Inclusv : A jobs board for People of Color; Inclusv breaks down the barriers that have traditionally prevented people of color from advancing in the political sector
The first site, Progressive Data Jobs, I have found to be the best jobs board in the field. Practically everyone I know that hires in this field posts there. It's also run by some of the best data folk in the game and contains a plethora of resources, including the famous progressive data salary survey. I would also recommend signing up for All Hands (they are also run by some great folk) so that folk hiring for progressive data jobs can proactively reach out to you.
We also have our own list servs, but these often require references to join. There are list servs for everyone working in progressive data, for women and non-binary people, for Black data practitioners, for Latinx data practitioners, and many more that I am unaware of. If you are interested in joining a list serv, please reach out to me and I'll see what I can do.
So now we know a little bit more about progressive data and can start applying to jobs, but the next question I tend to get asked is “what experience do I need?” What is the skill set needed to work in this field? The answer to that question, as annoying an answer as this is, is that it depends on the job you are applying to. One way to get a sense of the skillset you’ll need to get a job in this field is to do the following exercise:
Pull up one of the progressive data jobs boards I listed above
Go through the jobs and save/PDF the ones that look interesting to you. I say “save” and not bookmark because job listings get taken down. You’ll want to keep these.
Do this until you have about 5-10 jobs that you’d like to have.
Copy and paste the requirements section of the job into a document until you have one long blob of text that combines all the requirements across all the jobs you saved.
Take that blob of text and put it in a word cloud.
See what words pop up! Do you gravitate to jobs that mention SQL a lot? Dashboards? VAN? Note these keywords. If you don’t know something, ask a friend (Googling can sometimes be a lost cause in this field).
That being said, there is a set of skills that is common for folk seeing their first analytics job. It is very common for someone's first job in this space to be an analyst, regardless of what their title may say. An analyst is someone who works to clean messy data and make meaning of it, often by making reports and dashboards. You can find analysts everywhere in progressive data across all the entities I listed. Starting as an analyst is a great way to get your foot in the door and expose yourself to the other roles in this field.
Getting a job
So how do you get that first entry level job? I'm going to lay out a few skills that I look for as a hiring manager.
This is the holy grail. If you do one thing, my god let it be this. Learn SQL. Learn it well. A lot of folk I run into think they know SQL because it was introduced in a college course or they know the based "select from where" syntax. SQL is a deceptively powerful language and the lingua franca of my corner of the progressive data world. SQL is what let's you get data our of a warehouse (often where data is stored) and turn it into meaningful insight. We build reports and dashboards using SQL. When a coworker comes to us asking "how many doors did we knock on that race?" or "what is our email unsubscribe rate?", we answer that question using SQL.
What does "learning SQL" tangibly look like? To me it means writing in clean, well-named CTE; feeling comfortable writing window functions and knowing when to use them; and have opinions on how to best clean data in SQL. I tend to advise aspiring data folk that the mark of a beginner SQL user and an intermediate SQL user is their ability to use CTEs.
But writing SQL often requires access to a data warehouse, which is hard to come by if you're just learning on your own. The two big data warehouses in the field are Amazon's Redshift and Google's BigQuery. BigQuery offers a free version with public data sets so you can get your hands dirty fast, and it is worth mentioning that the Democratic National Party and a good chunk of the field use their BigQuery instance, called Phoenix.
Below I'm sharing some resources that have helped me learn SQL. But, the honest truth is that you'll learn SQL the more you write it. Writing more code, hitting more errors, and resolving those errors is truly the best way to pick up SQL, or really any programming language.
Some resources for learning SQL:
Mazur’s Style Guide (the one that most people use)
Brooklyn Data Co Style Guide (my preferred style guide)
If you read that and thought about the thing you drive, then this is for you. VAN is the near-universal database that campaigns and organizations use to conduct their organizing. It is the tool that organizers use to track the conversations they have at the door, our way of pulling a list of voters to target from the voter file, or a way to store information on volunteers and members. Entry-level data jobs often ask for VAN experience, but getting that experience can be difficult if you are new to this field.
The best advice I have for getting VAN experience if you have never touched VAN is to volunteer on a campaign. Go knock some doors! Go talk to some voters! Campaign experience, or as data practitioners call it—"domain expertise"—is invaluable. You will be a far more powerful data professional if you understand what you are using your data for. There is no substitute for getting your hands dirty on a campaign, electoral or issue advocacy. Along the way, you will get exposed to VAN and other tools of the trade, start to build relationships with folk who work in this space, and experience first hand how crucial data analytics and engineering can be.
It should come as no surprise that just as we have our own jobs boards and vendors that we also have our own trainings. The trainings I list below are designed to train data practitioners to work on campaigns and/or in progressive politics.
Trains people to join campaigns. Has a Data Director track.
Data and Analytics boot camp for campaigns and progressive tech
Generation Data is a mission driven non-profit. Our focus is training the next generation of leaders in the progressive data management community.
Bluebonnet places talented data folk with campaigns to give aspiring progressive data professionals real-world experience. Alumni have access to a vast network and opportunities to land full-time positions.
Even more resources
Finally, I have compiled a list of all the technical and progressive data resources I find useful. In many ways, this Google Doc is the precursor to this blog. It was the resource I shared with folk when they came to me seeking advice.
Applying for jobs
If you get really good at SQL, spend a cycle volunteering or working on a campaign, and expose yourself to progressive data tools (like VAN or Civis), you are set up to land an entry-level job in this field. Look for titles like data manager, data coordinator, or data analyst. And be sure to read the job description and check the requirements! A job titles "engineer" may be more suited for someone with an analysts' skill set if the job description talks about SQL, reporting, and dashboarding.
At this point, you should know a little bit more about progressive data, know where to look for a job, and have a sense of what skills are needed. To wrap up, I'm going to touch on job hunting advice.
I have written many, many resumes and have helped edit even more. I give the same advice to everyone when I give resume advice:
"Resumes are highly personal. If there was one true right way to build a resume, we would all be using the same template. The truth is there are a few golden rules of resume writing, and the rest is up to taste. What I'm about to say is my taste. I recommend that you run your resume by 3-5 people and determine what advice you agree with and what you can leave behind."
Here is my standard resume advice:
Your resume only has to be good enough. If you are spending 20, 30 hours perfecting your resume, you are overdoing it. The resume and cover letter need to be good enough to get you that first call back and otherwise does not need to be perfect.
The purpose of your resume is to show that you have the experience to do the job you are applying to. To that end, you only need to list relevant experience on your resume. Your resume should not be a litany of everything you have ever done. Your bullets should speak to either the data work you have done or the campaign and organizing experience you have.
White space is good. The fun fact that says something like the average recruiter spends 6 seconds looking at your resume? That's mostly true. You need to design your resume to be skimmable. That means having white space. If your resume is a wall of tiny, tiny text that the reader has to squint at to read, consider removing some bullets and introducing more whitespace.
Move your education to the bottom. It's usually the least relevant. And while we're here, you don't really need to list your GPA on your resume. If it's a 4.0, go for it. Otherwise, it's perfectly fine to leave it off.
Your bullets should speak to the impact you made. So instead of listing off your responsibilities (made dashboards, wrote reports, supported organizers, etc), you should talk about the difference you made. Oftentimes, we phrase our work as saving our teams money, time or resources. Did you build an automations that saved someone from having to manually enter data? Did you build a pipeline that helped someone get access to more clean data? and what did they do with that access? You didn't just build a dashboard, you delivered never-before-seen insight into some part of your work that allowed someone to make X decision.
If you are writing a resume and want feedback, fill out the contact form on my website.
I love writing cover letters. That may shock you, but hear me out. The cover letter is your chance to express your enthusiasm for the job. It allows you to be more than just your resume and really speak directly to the hiring manager. With a cover letter, you can explain gaps in your resume, talk about your passion for the work, and share your career desires and ambitions. I am a firm believer that you should always write a cover letter. And, I hope that one day you too learn to love the art of the cover letter.
Here is my standard cover letter advice:
In almost all cases, a real human being is going to read your cover letter. That means you should write your cover letter like a real human being is going to read it. When I write my cover letters, I like to imagine I am in the interview and I am asked "Can you tell me about yourself?" Whatever my answer to that question, I write it down in my cover letter.
A lot of cover letters fall into the same pattern and speak generically about the job, likely 1) due to the standard cover letter advice that is near ubiquitous across the Internet and 2) folk reusing the same cover letter for all their applications. You should write a unique cover letter for each job.
You want to make it clear to the reviewer that you read the job description and are applying for that job. To that end, if the job description says "1-2 year experience cleaning messy data", you should write about a time you had to clean messy data and how much you enjoyed it (even if it was hell at the time). If the job description talks about building ELT syncs, reflect that in your cover letter.
I once applied to a job in college to be a data assistant for a local city office. From reading the job description I got the sense it would be a lot of detail-oriented, spreadsheet work. I wrote my cover letter recounting a story from my teenage years when I would spend hours at my computer organizing my vast music collection. I would meticulously research each and every album in my 3,000+ collection and add the appropriate record label, genre, and release year into the metadata of the file. Looking back, it is no shock that I work in data. But back then, that was all the experience I really had to prove to this office that I could be a data assistant. I wound up getting the job, and a year later they personally emailed me to ask me to apply again. While I don't think my cover letter alone got me the job, it definitely got me noticed and remembered.
Acing the interview and other thoughts
I know I have some useful things to say about interviewing because I have gotten a handful of jobs in this space, but I have also bombed some interviews. I'm certainly not the expert here, but I have hired for many data and tech positions. And I can speak to my experience as a hiring manager.
The number one thing I look for as a hiring manager is enthusiasm and a willingness to do the job. I look for people who are excited to do the work, and to do the work of the job that I am hiring for. If you are applying for a Data Engineer position but spend your cover letter and phone screening talking about your love of building machine learning models, you may have enthusiasm but lack alignment on the job at hand. The best candidates are those that understand the job in front of them and speak to their excitement to do that job. A lot of the entry-level jobs in the space will have you wrangling mountains of messy, chaotic data, pulling numbers for key stakeholders, and building dashboards. If I got on an interview with someone who maybe lacked experience but spent the interview talking about how much they loooooved cleaning data or how beamed with joy as they described a dashboard they built, I would be more inclined to hire them than say someone with more experience but lacked the desire to do the job at hand.
And, part of having a "willingness to do the job" comes from understanding the job at hand, which means not only reading the job description but asking good questions during your interview. Some good questions to ask in an interview include:
Who does this person report to?
Where does the data team sit in the org?
Do you have a data warehouse?
What is your tech stack? What organizing tools do you use?
What are the organizations' priorities for the year?
How many other data staff are on the team?
What are the opportunities for growth in this position?
Will I have access to a professional development fund?
How does the team manage requests from other staff?
By now you should know
The key players in progressive data
What kinds of jobs exist in this space
What skills you should develop or highlight
Where to go to learn more skills
Where to search for a job, when you are ready
How to prepare your application materials
There is so much this post does not touch on, but I hope that you have a little more insight into this field than you did. And, if I sent you this post, you should feel free to reach back out with any questions and to schedule time with me if you still want to meet.