Extract. Transform. Read.A newsletter from Pipeline: Your Data Engineering ResourceHi past, present or future data professional! Somewhere along your professional development journey someone lied to you. They told you to crank out resumes because no one reads cover letters. This couldn’t be further from the truth as 87% of hiring managers read cover letters. Such a high read rate represents a compelling opportunity to sell your data skills and showcase a bit of personality. The problem? Those pesky 3 paragraphs take way too long to write—as long as 30 minutes per job. Assuming you’re applying to 3-5 jobs per day, you’re looking at 2.5 hours of cover writing time. Earlier this year, while helping a friend apply for data science positions, I created a simple Python script to auto-generate cover letters based on input. In addition to generating a cover letter based on my more than 500 hours as a career advisor, it will convert your output to a PDF, the preferred format for cover letters and resumes. Even if this helps you generate cover letters faster, you might want to think twice about “spamming” your resume/cover letter. Only use a bulk application method if:
Since I’m not trying to ask you to spend 30 minutes on this email, here are this week’s links:
Finally, this week is significant to me because 9/13 marks 3 years in data engineering. Read my story and advice for following a similar path. Why I Nearly Turned Down A 30k Raise And A Data Engineering Job Questions? You know where to find me: zach@pipelinetode.com. Until next time - thanks for ingesting, -Zach Quinn |
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.
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...
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! If you live in the U.S., this week marks the end of back to school season; though, if you’re like my southern relatives, you’ve been back since July. The closest feeling most adults get to back to school (aside from the teachers), is starting a new job. While a new org, title and compensation package represents new opportunities, it’s also easy to feel like the “new kid”, which can lead to being...
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! I once participated in a remote job interview in which the interviewer was on the video call while driving... and smoking. While that instance was among the most memorable interview experiences (for the wrong reasons), I’ve had just as many interviews that have blended together and faded into the recesses of my mind. The common denominator, however, was the insistence on asking one question. The...