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 years, a start-up cliche was being the “Uber” of (product, service, etc.). Now, it seems like any content platform wants to be the “Tik Tok” of a given subject area. Case in point for the latter: A fun app I came across called, fittingly, “Gittok.”* Like Tik Tok, Gittok feeds users an endless stream of distraction but instead of dance challenges it serves up a random GitHub repository, like...
Extract. Transform. Read. A newsletter from Pipeline For a STEM discipline, there is a lot of abstraction in data engineering, evident in everything from temporary SQL views to complex, multi-task AirFlow DAGs. Though perhaps most abstract of all is the concept of containerization, which is the process of running an application in a clean, standalone environment–which is the simplest definition I can provide. Since neither of us has all day, I won’t get too into the weeds on containerization,...
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! From 2014-2017 I lived in Phoenix, Arizona and enjoyed the state’s best resident privilege: No daylight saving time. If you’re unaware (and if you're in the other 49 US states, you’re really unaware), March 9th was daylight saving, when we spring forward an hour. If you think this messes up your microwave and oven clocks, just wait until you check on your data pipelines. Even though data teams...