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.
[ETR #16] Better Data Solutions In 5 Steps
Extract. Transform. Read.A newsletter from PipelineHi past, present or future data professional! When you apply to data analysis, data engineering or data science jobs, you likely consider factors like company name, culture and compensation. Caught up in the excitement of a fresh opportunity or compelling offer you’re neglecting an important part of your day-to-day reality in a new role: What stage of data maturity the organization is in. If you’re looking for experience building something new from the ground up, you likely won’t find it in a company that has a years-old established cloud infrastructure. If you’re inexperienced, you might also feel lost in a company that is still conceptualizing how it is going to establish and scale its data infra. While I personally arrived at a team and organization in its mid-life stage, I’ve had opportunities to discuss, examine and advise those who are considering how they can make an impact at an earlier-stage company in both full-time and contract roles. This compelled me, after a transatlantic flight, to compile a framework you can use to conceptualize anything from an in-house data solution to full-fledged infrastructure. Phase 1 Discovery - Extensive, purposeful requirements gathering to make sure you are providing a solution and, more importantly, a service, to an end user. Phase 2 Design - You can’t begin a journey or a complex technical build without a road map; take time to make a wish list of must-have data sources and sketch your architecture before writing line 1 of code. Phase 3 Ingestion - Build your pipelines according to best practices with a keen eye on cost and consumption; expect this to take 6-12 months depending on your work situation. Phase 4 Downstream Build - Going hand-in-hand with requirements gathering, consider how your target audience will use what you’ve built; might it be better to simplify or aggregate data sources in something like a view? Phase 5 Quality Assurance And Ongoing Tasks - Even though your pipelines and dashboards will be automated initially, nothing in data engineering is 100% automated. Components will break. You’ll be expected to fix them. And assure it doesn’t happen again. These 5 phases aren’t meant to be strict rules for building data infra. But they should get you thinking about how to build something purposefully so you can spend your time dealing with angry code–not stakeholders. Dive into the framework here. Here are this week’s links:
Until next time–thanks for ingesting, -Zach Quinn |
Extract. Transform. Read.
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! 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...
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...