From Data Analyst to Data Engineer: My 12-Month Self-Study Roadmap
The author, a data analyst and IT System Analyst, shares their 12-month self-study plan to transition into data engineering, driven by curiosity about data infrastructure and career growth. They aim to learn data engineering tools and build projects independently, despite not using them in their current job. Publishing the journey publicly serves as a commitment to consistency and accountability.
- ▪The author has experience in data analytics with skills in SQL, Power BI, Python, Pandas, NumPy, and data cleaning.
- ▪Their motivation includes understanding data pipelines, storage architecture, and staying relevant amid AI advancements in analytics.
- ▪They are following a self-directed, public learning roadmap inspired by Data With Baraa, with no on-the-job support or team collaboration.
- ▪The journey is pursued outside of work hours, aiming for three to four hours of study daily, with public writing used as an accountability tool.
- ▪The author acknowledges challenges such as shiny object syndrome, maintaining consistency, and work-life balance during the transition.
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| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/from-data-analyst-to-data-engineer-my-12-month-self-study-roadmap/ |
| Publication time | Sat, 16 May 2026 15:00:00 +0000 |
| Retrieval time | 2026-05-16T15:25:18.886Z |
| Last seen | 2026-05-16T15:25:18.886Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | hZeprlJRBYzp |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Data Engineering From Data Analyst to Data Engineer: My 12-Month Self-Study Roadmap The exact tools I'm learning, the projects I'm building, and the mistakes I'm already expecting to make Ibrahim Salami May 16, 2026 10 min read Share Generated with Gemini AI To be honest. A part of me started this journey because data engineering is one of the hottest and highest-paying careers right now. I’m not going to pretend that wasn’t a factor. But there’s more to it than that. I’ve been learning data analytics for a while now. SQL, Power BI, Python (Pandas, NumPy, a little Polars), data cleaning, EDA. You name it, I’ve been in the weeds with it. And I genuinely enjoy it. But somewhere along the way, I started getting curious about what happens before the data lands on my desk.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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