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Learning Data Engineering in Public: One Month Reflections

Towards Data Science •
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The author marks one month since publishing a self‑study roadmap that charts a 12‑month roadmap from analyst to data engineer. In that time he posted three write‑ups: an ETL pipeline built scratch, a production‑ready SQLite version and a scheduling setup via GitHub Actions. Those pieces document progress but not the month’s emotional texture. The write‑ups also expose hidden non‑technical lessons about trust, resilience and persistence.

Feedback from strangers initially fueled his momentum, but the buzz faded as his day job grew heavier and a new Laravel assignment ate into study time. The looming data engineering role felt distant, turning ambition into fatigue. Instead, a single comment echoing his own roadmap nudged him back on track, reminding him to ignore external opinions. He also considered AI engineering briefly.

He now treats the roadmap as a flexible guide, letting the pipeline dictate the next skill—SQL, Python, Git—rather than following a strict sequence. Small, weekend‑sized projects replace grand, resume‑building builds, fitting around a 9‑to‑5 and Laravel deadlines. Each finished mini‑pipeline yields a concrete lesson, and reader thanks provide the strongest incentive to keep coding.