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Hi fellow data professional! Once, during a virtual interview, I had to nod politely as my interviewer apologized for coughing after their cigarette. Oh, and to make this situation even more cringe—they were driving. Some industries design stressful interview processes to psychologically test a candidate’s poise under pressure. Luckily (for the most part) the software engineering field is not included in this basket of high-stress tests. Sure, we are subjected to moderate stress in the form of whiteboard sessions or system design discussions, but nothing beyond the expected pressures of on-the-job development. So when something doesn’t go quite right during an interview, it’s understandably stressful. Handling unforeseen obstacles requires a mixture of calm and an ability to “read the room” to determine the severity of the interruption. In low stress situations, like when I forgot how to share my screen, it’s perfectly acceptable to call yourself out as long as it is done tactfully like: “Sorry; I’ve been coding all day and I’ve forgotten how to share my screen.” Bookending your humble admission with a statement about being so dedicated to work that a mundane task has slipped your mind can actually work in your favor. But if you find yourself in a “fish out of water” moment, like when I had a virtual interview my interviewer was clearly conducting while driving, it can be a bit difficult to maintain professionalism. In broadcast journalism I learned that the mark of a good reporter is being able to maintain composure in the face of technical failure. A trick I used if a mic wasn’t working or remote connection couldn’t be completed is to count backwards from 10 and try only one more time. If you can’t resolve an issue on 2 attempts, it’s best to quit and alert the other party to the issue rather than extending the awkwardness beyond a reasonable timeframe. Something candidates tend to forget is that you can end an interview for any reason; you’re not trapped. Personally, I’ve ended two interviews in the first 5 minutes because I felt that the recruiters misrepresented the role in the posting. Remember, when you accept a cal invite, an interview is your time too. Thanks for ingesting, -Zach P.S. One of the most deceptively difficult interview steps is nailing the phone interview; I wrote a guide to prepare you for technical phone screens in just 30 minutes. Medium | LinkedIn | Ebooks |
Reaching 20k+ readers on Medium and over 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.
Hi fellow data professional! On a recent holiday, a family member and I were strolling along a beach, talking about AI disruption (relaxing, I know). He, an attorney, assured me his job was AI-proof and jokingly offered to hire me when AI takes my data engineering job. If you ask executives at most companies, they’d find several flaws in that argument. Over 80% of technical executives, including Chief Data Officers and Chief AI Officers, consider data engineering to be an essential role...
Hi fellow data professional! Ken Jee, who you heard from last week, drops some sobering career advice in one of the earliest editions of AI Survival Guide: Making a senior-level tech role is no longer about advancement; it’s about survival. The post talks about the growing moat or "wall" between those breaking into the industry, those in entry-level roles and those in a mid-career phase. In the spirit of AI Survival Guide’s advice to bridge the gap separating the early and mid-career...
Hi fellow data professional!' Today I’m turning the newsletter over to my friend Ken Jee (writer of AI Survival Guide, creator of Newsletter Hero) to share how he cuts through the noise of shiny AI products to find tools that enhance technical work. My Simple Framework For Adopting AI Tools Ken Jee As new AI tools launch almost daily, a quiet tax is emerging. Decision fatigue. Every new model, agent, or workflow tool carries the same implicit question. Should I switch, or should I go deeper...