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 |
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Extract. Transform. Read. A newsletter from PipelineToDE Hi past, present or future data professional! Despite crushing autocorrect scenarios, most AI code assistants like CoPilot miss a critical step when helping developers of any experience level: Validation. Arguably, leveraging an AI Agent to validate a code’s quality is on the user. But a surprising amount of experienced programmers are taking the worrying approach of believing an AI’s first “thought” when it comes to code that will...
Extract. Transform. Read. A newsletter from Pipeline Hi past, present or future data professional! A data science manager recently gave me some blunt, liberating advice over coffee: “If a team lead really cares what cloud technology you know (AWS, GCP, etc.) and doesn’t consider transferable experience… run.” This critical advice, which informs the conclusion of my soon-to-be-released ebook on data engineering project development, cuts to the core of a major problem in data hiring: The...
Extract. Transform. Read. A newsletter from Pipeline Hi past, present, or future data professional! For all of the latest tech trends that come and go, one idea has always persisted in the tech world: Longevity isn’t cool. I say this because I recently hit a professional milestone: 4 years with my current organization. My career trajectory offers a counter-narrative to the “job-hopping is the only way to succeed” mentality. My goal isn't to convince you to be a “lifer,” but to demonstrate...