Analyst postings are keyword-dense, and the screeners at companies running Workday, Greenhouse or Taleo match hard skills literally. Name your tools exactly as the posting does: SQL, Python, Excel, and your BI layer (Tableau, Looker, or Power BI) belong in a dedicated skills section AND inside experience bullets where you used them — parsers weight both, humans only trust the second.

Spell out variants once: if the posting says "Power BI" don't write only "BI dashboards"; if it says "A/B testing" don't write only "experimentation." Literal matching is dumb, and it's the first gate.

Weak analyst resumes describe activity: "analyzed sales data and created dashboards." Strong ones follow analysis → decision → number: what you examined, what changed because of it, and the measured result. "Analyzed promotion performance across 84 stores; findings shifted $1.1M of budget to the two mechanics that lifted margin" survives both a screener's skim and an interviewer's follow-up.

If you're early-career and short on business outcomes, quantify scale instead: rows processed, users of your dashboards, hours automated away. Numbers about the work beat adjectives about you.

Projects earn their place on an analyst resume at every level — a public analysis with a link is verifiable evidence of the whole pipeline (data acquisition, cleaning, analysis, communication). One strong project with a live dashboard outweighs five course listings.

Certifications are supporting cast, not the lead: list Google Data Analytics, Tableau, or cloud certs in education/skills, but never let them displace demonstrated work. Hiring managers read certs as "trained," projects as "can do."