Data Analyst

This covers the full stack of SQL-first analytics work: writing performant queries with CTEs and window functions, profiling datasets for nulls and outliers, running hypothesis tests (t-test, chi-square), and packaging findings into stakeholder-ready recommendations. You get three Python scripts that catch SQL anti-patterns, generate data quality reports, and build summary tables. The workflow is structured around framing business questions as testable hypotheses, then walking through explore, analyze, visualize, and deliver. It's opinionated about chart selection and dashboard layout, which is helpful if you want guardrails. Best for analytics engineering roles where you're expected to own the full cycle from raw query to board deck.