[ETR #66] 3 Signs You’ll Stay At Your Potential DE Job


Extract. Transform. Read.

A newsletter from Pipeline

Hi past, present, or future data professional!

For all of the latest tech trends that come and go, one idea has always persisted in the tech world: Longevity isn’t cool. I say this because I recently hit a professional milestone: 4 years with my current organization. My career trajectory offers a counter-narrative to the “job-hopping is the only way to succeed” mentality. My goal isn't to convince you to be a “lifer,” but to demonstrate that with enough demonstrated competency, leadership advocacy, and organizational support, there are significant benefits to sticking around for a bit.

My first few months were a lot of me failing, but also learning. I had to get comfortable with production environments and CI/CD pipelines, and I had plenty of failed deployments. I dedicated extra time to upskilling, practicing SQL with complex data types, and building simple ETL pipelines. My favorite quote from this period came from a senior engineer who said, "Seniors aren't necessarily writing the cleanest code on the first try; they're just much better at covering and recovering from mistakes."

By my second year, I was a seasoned veteran on a still-new team. My career began as the team’s second hire, and I had the somewhat rare opportunity of growing alongside it. We hit major milestones, like building 500 pipelines. I even started to review technical applications, which was surreal for someone who could barely deploy a pipeline a year prior. The autonomy accompanying greater seniority (and resulting from some organizational shifts) was both challenging and rewarding, and it made me appreciate the consistency and stability of my role. As I entered my fourth year, my new manager’s support and the company’s flexibility, especially with a new baby on the way, truly underscored the value of staying with one organization.

How To Spot A Long-Term Opportunity Of Your Own

Unfortunately, before beginning a job, it is difficult to determine if this will be a professional home or simply a two-year stepping stone. Sites like Glassdoor can help, but companies with thousands of reviews can also trigger analysis paralysis.

I’ve found the following to be generally true–

  1. Companies with clear hiring processes and empathetic recruiters are generally good deals; I once hung up on a recruiter who didn’t even read my resume and tried to get me into a candidate pool below my current skill level
  2. Interviews that feel less interrogative and more dialogic signal managers who are open to conversation, feedback and may be genuinely passionate about their field
  3. Decision makers who follow up and show interest through thank you notes (rare but not unheard of from interviewers) and competitive offers that make it seem as if your hiring is a matter of urgency and priority

As a testament to those rules of thumb, today I’m still working in the same home office where I began my career four years ago. I’ve been able to play a significant role in building the data infrastructure for a 100-plus-year-old brand and enjoy professional mobility. Ultimately, while job-hopping has its allure, sometimes the best career perk is consistency.

Before You Go…

I’m (so close to) finishing a resource, a new ebook to help you get started and actually finish business-relevant data engineering portfolio projects that will help you land a long-term DE opportunity of your own.

I’ll be publishing it in October and announcing it in a future newsletter, but if you want early access, join the waitlist.

And if you want to read more about my career lessons so far, you can read the retrospective I published on my 4th anniversary as a data engineer.

Thanks for ingesting,

-Zach Quinn

Extract. Transform. Read.

Reaching 20k+ readers on Medium and nearly 3k learners by email, I draw on my 4 years of experience as a Senior Data Engineer to demystify data science, cloud and programming concepts while sharing job hunt strategies so you can land and excel in data-driven roles. Subscribe for 500 words of actionable advice every Thursday.

Read more from Extract. Transform. Read.

Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! A data science manager recently gave me some blunt, liberating advice over coffee: “If a team lead really cares what cloud technology you know (AWS, GCP, etc.) and doesn’t consider transferable experience… run.” This critical advice, which informs the conclusion of my soon-to-be-released ebook on data engineering project development, cuts to the core of a major problem in data hiring: The...

Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional!The most dramatic dataset in the U.S. right now is labor data. Marred by revisions, official firings and general distrust, when the dust clears, U.S. unemployment hovers around 4%. The tech sector, specifically, ticked up from an all-time low to 3%, with overall jobs and postings decreasing significantly. While 3% is far from the industry’s record 5.7% set earlier this year, it is concerning.Ok....

Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! I dreaded entering the job market after my data science master's. I felt like I knew more than a data analyst but less than a professional data scientist. I've since realized my program was more effective than I thought, but it couldn't prepare me for the key areas like cloud deployments and real-world problem-solving I had to learn on the job as a data engineer. And I’ve noticed these gaps in...