A data analyst turned IT system analyst outlines a public 12‑month roadmap to become a Data Engineering specialist, driven by curiosity about pipelines and rising AI automation. The plan starts with deepening SQL skills, then refines Python for production, and introduces Git for version control.

The next phase tackles distributed processing with Apache Spark and PySpark, shifting from single‑machine Pandas to cluster‑scale analytics. Followed by workflow orchestration via Apache Airflow, the author argues it offers industry‑wide, cost‑effective pipeline management.

Finally, the roadmap converges on the Databricks platform, positioning learners for roles that demand cloud‑agnostic data infrastructure. By documenting progress publicly, the author seeks accountability and a portfolio that outpaces résumé claims.

This structured, tool‑centric journey demonstrates that mastering pipelines, not just analysis, remains essential as AI handles more exploratory tasks.