A data warehousing specialist makes company data trustworthy and easy to use. You pull data out of source systems like billing, claims or CRM, clean and reshape it, store it in a well-designed warehouse, and keep it fast and correct for analysts. The core tools are SQL, dimensional modeling (Kimball star schemas), ETL/ELT pipelines, and a cloud warehouse such as Redshift, Snowflake or BigQuery. The path: learn SQL and relational basics, then modeling and pipelines, then build a real warehouse end to end, then scale it with orchestration, streaming and performance tuning. Most hires hold a bach
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Every stage from beginner to expert, with lessons, practice drills, projects, interview questions and free resources. Free with a Rolearch account.
Stage 1 of 3 · about 10 weeks
Write confident SQL, understand how relational databases store data, and script basic data movement.
SQL querying
Every warehouse task, from checking data quality to building transformations, is done in SQL. If you master joins and aggregation, everything else gets easier.
Relational database design
A warehouse is built on tables, keys and constraints. Understanding normalization tells you what the source systems look like and why you reshape them.
Python and Bash scripting
You will automate file pulls, call APIs and glue steps together. Python and shell scripts are the everyday tools for that.
Git and version control
Pipelines and SQL are code. Teams review, track and roll back changes with Git, and employers expect it on day one.
Then 2 more stages (about 40 weeks in all) with lessons, drills, projects, 6 interview questions with strong answers and free resources.