Data & Analytics · Mid level · Updated August 2026

    Data Analyst Resume Example

    A data analyst resume has one job: convince a skeptical reviewer that when you analyze something, somebody acts on it. The example below is built around that idea. Every bullet names a question, an analysis, and what changed afterward. Open it in the builder and rebuild it with your own numbers.

    Bullets that end in a decision

    Analysts produce charts, queries, and reports, but companies pay for decisions. The strongest analyst bullets follow a quiet three-part shape: the business question, the analysis that answered it, and the action that followed. The example's opening bullet ("traced 22% of churn to an onboarding drop-off; the fixes recovered $1.4M") tells that complete story in one line.

    Not every analysis ends in a headline number, and that's fine. A metric definition that ended weekly arguments, a report that stopped being wrong, an automated pipeline that gave you your Mondays back: these are all decisions someone made because of your work. Write the outcome, then attach the tooling, never the reverse.

    Analyzed, supported, assisted: verbs that bury your work

    The fastest way to sound junior is to describe activity instead of results. Verbs like "analyzed," "supported," and "assisted with" tell a reviewer you were near the work without saying what you changed.

    Do

    • Start with the finding or the outcome, then the method
    • Put a number on time saved, revenue found, or errors caught
    • Name who used your work: execs, ops, 60 weekly viewers
    • Show the question you were answering, not just the tool

    Don't

    • Open bullets with 'analyzed data to...' and stop there
    • Claim 'improved reporting' with nothing measurable
    • List dashboards as deliverables with no audience
    • Hide one real win under six routine duties

    Audit your draft by covering the first half of each bullet: if the remaining half says nothing concrete happened, the bullet needs rebuilding, not rewording.

    Ordering the toolkit: SQL first, then proof

    Recruiters scan the skills section against the job post before reading anything else, so order it by what analyst roles actually screen for: SQL first, then your BI tool, then Python and statistics. Group them so each cluster answers a different question about you: can you get the data, can you present it, can you go deeper when the question demands it.

    Then make the work history vouch for the list. A skills line that says Tableau means little; the example's bullet about a dashboard 60 leaders open every week settles it. If a tool on your list has no supporting evidence anywhere on the page, either find the story or drop the tool. And tailor per posting: a job ad that says Power BI deserves a resume that says Power BI, not "BI tools." Two adjacent titles have their own pages worth checking before you apply: governed reporting and semantic models are the BI analyst example's territory, and if the part of the job you like most is the tested models behind the dashboards, the analytics engineer example shows the resume that leans into it. Breaking into the field rather than mid-career? Start from the entry-level data analyst example instead. And with a target posting in hand, scan your resume for missing data analyst keywords before you apply: SQL-vs-Tableau emphasis differs posting to posting, and the scanner shows which terms this specific employer will search for.

    Frequently asked questions

    Do data analysts need Python, or is SQL enough?

    SQL is the non-negotiable core; most analyst screens test it directly. Python widens the roles you qualify for (automation, deeper statistics, messier data) but plenty of analysts are hired on SQL, Excel, and a BI tool alone. If you know some pandas, list it honestly as "Python (pandas)" rather than implying full engineering fluency.

    How do I take credit for a decision someone else made?

    Name the decision and your input's role in it. "Identified the two discount tiers losing money; both were cut the following quarter" is honest and specific. You don't need to have pulled the trigger; you need to show your analysis is the reason someone did.

    Should a data analyst resume link to a portfolio?

    Yes, if it holds real work. One public dashboard or write-up built on a real, messy dataset beats five course exercises. Reviewers rarely spend more than a minute there, so lead with your single strongest piece.

    Is Excel embarrassing to list?

    No. Excel still runs a huge share of business analysis, and hiring managers know it. It only reads poorly when it stands alone. Pair it with SQL and a BI tool and it signals range, not datedness.

    Ready to make it yours?

    Open this example in the builder, swap in your own work, and download a polished, ATS-ready PDF.

    Read the full example as text

    Maya Chen — data analyst resume example

    Denver, CO

    Summary

    Data analyst with seven years turning ambiguous business questions into analyses leadership acts on. Deep SQL, Tableau, and Python; known for metric definitions people stop arguing with.

    Work Experience

    Senior Data Analyst · Meridian Health Plans

    2022 – Present · Denver, CO

    • Traced 22% of member churn to an onboarding drop-off through cohort analysis; the resulting fixes recovered an estimated $1.4M in annual premium revenue.
    • Built the executive Tableau dashboard now opened by 60+ leaders weekly, replacing five conflicting reports with one certified source.
    • Standardized 30 core metric definitions with finance and ops, ending the recurring 'whose number is right' meetings.
    • Designed and read out 12 A/B tests on member outreach; the winning cadence lifted preventive-visit bookings 9%.

    Data Analyst · Rowan Outdoor Co.

    2020 – 2022 · Denver, CO

    • Analyzed promotion performance across 800 SKUs and cut the two discount tiers that lost money, adding 3 points of gross margin.
    • Automated the weekly trading report with SQL and Python, cutting prep time from six hours to twenty minutes.
    • Turned repeat ad-hoc requests into a self-serve Looker Studio layer that cut ticket volume 40%.

    Research Analyst · Summit Peak Research

    2019 – 2020 · Denver, CO

    • Programmed and fielded consumer surveys for eight CPG clients; cleaned and weighted 10k+ responses per study in SQL and Excel.
    • Built the pre-launch logic checklist that cut questionnaire errors to near zero; it became the team standard.

    Projects

    Denver rent tracker · SQL, Tableau

    • Public Tableau dashboard tracking listing prices and days-on-market across metro Denver ZIP codes, refreshed weekly from public listings data.
    • Picked up by a local housing newsletter; 15k views to date.

    Education

    B.A. Economics · University of Colorado Boulder

    2015 – 2019 · Boulder, CO

    Certifications

    Google Data Analytics Professional Certificate · Google

    2020

    Tableau Desktop Specialist · Tableau

    2021

    Languages

    • English · Native
    • Mandarin · Professional

    Skills

    • Analysis: SQL, Python (pandas), Excel, Statistics
    • Visualization: Tableau, Looker Studio, Power BI
    • Practices: A/B testing, Cohort analysis, Metric design, Data storytelling