What this role’s resume is actually judged on — the language postings use, what a reviewer looks for in the first ten seconds, and the specific way Data Analyst resumes go wrong.
A first pass takes seconds and is looking for three things. If they are not near the top, the rest of the page rarely gets read.
Most resumes fail one bullet at a time. The difference is almost always specificity — a number, a constraint, or a consequence.
Created dashboards and reports for business stakeholders.
Replaced the weekly manual sales pack with a self-serve dashboard, saving the team four hours a week and surfacing the regional gap that redirected Q3 targets.
Terms that recur in real postings for this role. They belong in the bullet that proves them, not in a list at the bottom — our own checker weights requirement coverage at 40% and raw keyword matching at 20%, and most serious systems make a similar trade.
A keyword you cannot defend in an interview is worse than a missing one. How the format checks work.
Tableau, Power BI, Looker, Excel — every analyst resume has these. What differentiates is a specific business question you answered and what changed because of it. Tools are the how, and the how is the least interesting part.
Analyst loops test whether you can turn a vague business question into a specific, answerable one. SQL is table stakes; framing the question and communicating the caveat is what separates candidates.
AUC where the business metric should be
No mention of what happens when a pipeline breaks
Research projects presented as production work
No evaluation story anywhere on the page
Reading as an analyst who happens to write dbt
Listing technologies you have touched once
Generic resume advice only goes so far — what matters is whether this resume covers this posting. Paste both and get requirement-by-requirement coverage, the keywords you are missing, and the format problems a parser will hit. The first check is free.