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! 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...
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! As difficult as data engineering can be, 95% of the time there is a structure to data that originates from external streams, APIs and vendor file deliveries. Useful context is provided via documentation and stakeholder requirements. And specific libraries and SDKs exist to help speed up the pipeline build process. But what about the other 5% of the time when requirements might be structured, but...
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! To clarify the focus of this edition of the newsletter, the reason you shouldn’t bother learning certain data engineering skills is due to one of two scenarios— You won’t need them You’ll learn them on the job You won’t need them Generally these are peripheral skills that you *technically* need but will hardly ever use. One of the most obvious skills, for most data engineering teams, is any...