A data engineer builds the pipelines and warehouses that move data from messy source systems (billing, claims, apps, logs) into clean, trusted tables that analysts and models can use. Think of yourself as a plumber for data: nobody notices when it works, and everyone notices when it breaks. Your day is SQL, Python, modeling tables, scheduling jobs, checking data quality and fixing broken loads. Most US employers want a bachelor's degree (78% of workers have one), but a portfolio of working pipelines counts heavily, and every tool in this roadmap has a free path. The order matters: SQL first, t
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Stage 1 of 4 · about 12 weeks
Write confident SQL (joins, GROUP BY, window functions, CTEs), learn Python basics, use Git and the Bash command line, and understand how relational databases store data. Practice: load a public CSV (e.g. NYC taxi trips) into PostgreSQL and answer 20 business questions with queries.
Confident SQL: joins, aggregation, CTEs and window functions
SQL is the daily language of a data engineer. Every warehouse task in your job description (mapping data, verifying quality, transforming, extracting) is done with queries. Interviews almost always include a live SQL test.
How relational databases store and protect data
A warehouse is a database you design. If you understand tables, keys, types, constraints and indexes, you can build structures that stay accurate and fast, and you can troubleshoot when they do not.
Python basics for data work
Python is how you extract data from files and APIs and glue steps together. Your job description includes writing and modifying programs, and every later stage (Airflow, Spark) builds on it.
Git and the Bash command line
Pipelines run on servers with no mouse. Bash lets you move through files and automate; Git lets you track changes, recover from mistakes and share work. Your GitHub portfolio is what gets you interviews.
Then 3 more stages (about 74 weeks in all) with lessons, drills, projects, 10 interview questions with strong answers and free resources.