Extract. Transform. Read.A newsletter from Pipeline Hi past, present or future data professional! A peer of mine once revealed the reason they were sleep deprived: They were up past midnight writing ad hoc SQL queries with a c-suite leader literally hovering over their shoulder. The visibility of data analysts (like the one in the anecdote) and data scientists’ products, dashboards and ML models, means they are often the first on a Business Intelligence team to be bothered when something “looks weird.” This deference to the other more visible data teams shouldn’t stop you, the ambitious engineer, from taking on an important but unofficial role: SME, a.k.a. Subject Matter Expert. Being able to not only tell a stakeholder when a data source loaded (or didn’t) but also being the go-to person for questions, vendor outreach and general support, makes you an invaluable resource that goes beyond your job title and ability to “crank out code.” In a bumpy job market, this is key to cementing yourself as a must-retain staff member. To be a true SME you need to not only know your data, but also understand the larger business context which your work contributes to, which typically breaks down into: Resource conservation, performance optimization and revenue generation. For a working data engineer, going from DE to SME involves stepping outside of your comfort zone by:
If you’re not currently working in a data engineering role or are still in the job search phase, you can be an SME by narrowing your search to focus on industries or “domains” in which you have proven experience. For more information on how to apply that experience, read this guide. Anticipating business and stakeholder needs means less late nights and, more importantly, a little breathing room. Thanks for ingesting, -Zach Quinn |
Top data engineering writer on Medium & Senior Data Engineer in media; I use my skills as a former journalist to demystify data science/programming concepts so beginners to professionals can target, land and excel in data-driven roles.
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! For data engineering, a profession built on principles of automation, it can be counterintuitive to suggest that any optimizations or “shortcuts” could be negative. But, as someone who was once a “baby engineer”, I can tell you that a combination of temptation and overconfidence will inevitably drive you to say “I could do without x development step.” Doing so increases reputational risk (loss...
Extract. Transform. Read. A newsletter from Pipeline. *Today's edition was initially published on Medium on 12/10/24 Hi past, present or future data professional! I’ve recently been honing a data engineering skill that might not occur to you—drawing. When I first started my data engineering job 3+ years ago, any description or information related to my code would be in written form. This meant everything from README documentation to illegible legal pad scribbles would be all I had to inform...
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! If you haven’t heard "Happy New Year" enough in the past week… let me be, hopefully, the last to say it as we embrace all 2025 has to offer. Beginning a new year comes with the inevitable conception (and ultimately ignorance) of a new year’s resolution. Instead of focusing on one abstract goal to improve, I’d like to suggest, instead, that you form lasting habits, especially when it comes to...