How I switched from a career in public relations to data engineering

Dec. 9, 2020

In the past five years, I pivoted from working at a public relations agency for tech companies to working in-house with data. I went from cold-calling journalists to writing code every day. Whenever I mention this at networking events or to new co-workers I am met with questions such as, “Why did you make the switch?” or “How hard was it?”

From agency to analyst
In college, I majored in Communication Studies where I was able to critically analyze media and how it shapes the world around us. I was better at math and science in high school, but I wanted to be more introspective and engaged with the world in an academic setting. I preferred to express my thoughts and feelings in a term paper rather than memorizing a bunch of formulas and sitting for a test that could make or break my GPA.

At Northeastern, I secured three co-ops in various marketing and communications roles -- my last co-op was at a PR agency for early-to-mid stage tech startups. This last co-op helped secure my first job out of college at a tech PR agency where I worked with clients in the finance, aviation, and collaborative robot spaces.

I improved my writing and project management skills in this position, but over time I realized that I enjoyed the measurement aspect of how my content performed: Were people even reading what I wrote? What is the profile of these readers? Was I influencing customer actions for my client? It felt great to secure a win, but if no one was reading or engaging with my content then what was the point?

After gaining access to Google Analytics for one of my clients, I was amazed at how much data is tracked as visitors navigate through a website and how data visualization can tell a compelling story. As I dove deeper into analytics, crafting persuasive pitches for an increasingly shrinking journalism pool started to lose its appeal.

Pivot, pivot, pivot
In order to secure a job as a data analyst, I realized I needed to beef up my resume with more relevant experience. I applied to many jobs without even securing a basic phone interview -- the recruiters hiring for the analyst jobs I applied to most likely saw “PR professional” and tossed out my resume immediately. Having just graduated from college, I wasn’t ready to take on a lot of debt to pursue a full-time degree so I enrolled in a part-time data analytics bootcamp. In the span of four months, the course blazed through the fundamentals of Excel, SQL, R, and Tableau. The course met once a week in person for three hours with lengthy case studies to complete between each session.

My days were extremely long during this period of time: I had just secured a new full-time job via a college friend, and my commute was an hour and a half one-way since I moved back home in the suburbs to save money on living expenses. Luckily these long train rides allowed me to complete assignments to and from work, but I stayed up late on many occasions to get the work done. I admire people that can complete full degrees while working full-time and starting families.

Upon completion of the bootcamp, I landed a new job as a Junior Data Analyst at a food delivery startup. To this day I credit the bootcamp for securing the job. During my time there I touched all aspects of the data pipeline: data engineering, analysis, data science, and visualization. People are often confused about the distinguishing responsibilities between a data analyst, data scientist, and data engineer. From my perspective, a data analyst manipulates data to explain the past; a data scientist models data to predict the future; and a data engineer enables these two functions by building scalable solutions to lower latency and ensure data accuracy.

After two and a half years as a data analyst, I decided to specialize and grow as a data engineer. As I started playing around with the command line and git, I continually wanted to submit PRs that manipulate data upstream to better suit my analysis downstream; I became fascinated by data infrastructure and automating tasks to make my job easier. I was lucky to have coworkers that were willing to mentor me and answer any questions I had while branching out from my job responsibilities as a data analyst.

What’s next?
Today I am a data engineer at Toast where I use SQL and Python to transform data using many engineering concepts including data lakes, task orchestration, CI/CD, virtual machines, and so much more. I do not regret making the decision to work with data, and I worked extremely hard to make a career change this early in my life. I am thankful that I continue to work with individuals that are advocates for new opportunities and encourage me to expand my skill set. While I miss the thrill of securing coverage for my clients and creating content, I am happier with more structure in my workday and the perks of a coding job at a tech startup.

There are many ways I can grow as a data engineer in my day job: devops, data streaming, data architect, etc. However, I have also enjoyed learning web developer skills outside of work. This website was coded by me using Python and the Django framework! I don’t know exactly where my career will move next, but I’m confident that I can figure it out as I find new areas of interest in the engineering discipline.

If you are looking for advice on how to pivot into a data career or would like to work together, contact me on LinkedIn or via my contact form. If you want to get started as a data engineer, I recommend learning SQL first and reading The Data Warehouse Toolkit, which is the “Kimball“ method of data modeling:

About Me
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James Roselle is a data engineer based in Boston.

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