15+ Data Analyst Portfolio Projects That Get You Hired

Strong portfolios anchor on SQL proficiency first. Recruiters skim for evidence you can write multi-table joins, window functions, and CTEs against a real dataset — not just SELECT statements. At least one project should be SQL-heavy with the queries visible in a GitHub README or as part of a documented case study, because a SQL test is the single most common screen in data-analyst interviews.

Next comes visualization and storytelling. A clickable Tableau Public or Power BI dashboard beats a static screenshot every time. Pair it with a short narrative: what question you asked, what the chart actually shows, and what decision a stakeholder should make. Hiring managers consistently report that the gap between juniors and seniors isn't tooling — it's the ability to explain a chart in one clear sentence.

Finally, ground every project in business context. A churn analysis with no recommendation is a homework assignment; a churn analysis that ends with "cut onboarding from 5 steps to 3, projected +8% retention" is portfolio gold. Make your work discoverable: a GitHub repo with a clean README (problem, data, approach, insight, recommendation), a live dashboard link, and 2–3 inline screenshots. That combination — SQL + dashboard + narrative + business decision — is what consistently moves analyst portfolios from "noted" to "let's interview."