How to write a data analyst resume
By Aaron Cao · Updated
Lead with analyses that changed a decision, not tools you have used. Each bullet should name the question, the data and method, and the business result. Group technical skills by type, such as SQL, Python and BI tools, link a portfolio for entry-level roles, and keep a single-column layout.
What does a hiring manager look for in a data analyst resume?
It is tempting to fill the page with every tool you have touched. This section explains what the reader actually scans for, so you can decide what stays. They want evidence that you turn data into decisions, the tools the posting names, and signs that you can explain a result to people who do not read SQL.
Data analyst roles vary: product and marketing analytics, finance and operations reporting, business intelligence. Whatever the flavor, the reader looks for the business domain you worked in, the questions you answered, the kind of data you handled, your stack, and how the results reached people, such as a dashboard, a report or a presentation.
A reliable order is a two-line summary (optional), experience, projects, skills, then education and certifications. For entry-level candidates, projects move above experience.
How do you write bullets that show impact?
Use one formula: the action, the question and data, the method or tool, then the result. Put the result where a skimming reader sees it.
- Weak: "Created dashboards in Tableau."
- Strong: "Built a Tableau churn dashboard from subscription and support data, which the retention team used to choose the customer segments for a win-back campaign."
- Entry level: "Analyzed a public bike-share dataset in Python to find which stations ran empty at commute peaks; published the notebook and a short written summary."
Use real numbers when you have them and can defend them in an interview. When you do not, describe the decision or the audience instead ("used weekly by the sales leadership team"). An invented percentage falls apart at the first follow-up question.
Which skills and projects should you include?
Group skills by type so the reader finds them fast: querying (SQL), programming (Python or R), spreadsheets (pivot tables, lookups), BI tools (Tableau, Power BI, Looker), statistics (A/B testing, regression) and data platforms (for example BigQuery or Snowflake). List only what you could use in a live SQL screen or a take-home task, and put the tools the posting names first.
Projects carry the most weight when your job title does not say analyst. A marketing coordinator moving into analytics had no analyst role to list, so the resume was rebuilt around two projects: a campaign attribution analysis done in spreadsheets at the current job, and a SQL project on a public e-commerce dataset. Each bullet named the question, the method and what the finding changed, and the recruiter screen spent most of its time on those two lines.
The resume builder has a single-column template with standard headings, so the layout parses cleanly for applicant tracking systems.
How does your resume carry into the interview?
Every line is a question you may be asked. If you cannot talk for two minutes about a project, cut it or learn it. More guidance on formats, length and screening is in the resume hub.
SubcueAI's resume builder includes an AI Optimize action that rewrites your summary and bullets to be tighter and shows a before-and-after diff you approve; it rephrases what you wrote without inventing experience, and it runs on credits. Mark the resume active and the mock interviewer reads it alongside the job description, so practice questions probe the exact projects you listed. SQL screens, take-home analyses and proctored assessments are yours to do on your own; a live assistant does not belong in them.