Data & AI
Data Analyst Resume Keywords and Skills
By the StructuredCV team · Updated
Data analyst job descriptions screen for SQL first, then a BI tool (Tableau, Power BI or Looker), spreadsheet depth in Excel or Google Sheets, and often some Python or R. What separates shortlisted resumes is evidence that the analysis changed something: a report that replaced manual work, a finding that shifted a budget, a dashboard a team checks every week. Write bullets that name the data, the tool, the audience and the decision, and quantify time saved, revenue or cost affected, or adoption. Keep the skills section honest and specific about the functions and techniques you actually use.
Hard skills and keywords for data analyst resumes
Include the ones you have actually used, in the wording the job description uses.
Querying & data wrangling
- SQL
- Joins, CTEs and window functions
- Python (pandas)
- R (tidyverse)
- Data cleaning
- ETL/ELT pipelines
- dbt
- Data modeling (star schema)
BI & visualization
- Tableau
- Microsoft Power BI
- DAX
- Looker and LookML
- Looker Studio
- Dashboard design
- Data storytelling
- KPI reporting
Statistics & experimentation
- Descriptive statistics
- A/B testing
- Hypothesis testing
- Regression analysis
- Cohort and funnel analysis
- Forecasting
- Customer segmentation
Spreadsheets & business tools
- Microsoft Excel
- Pivot tables
- XLOOKUP and VLOOKUP
- Power Query
- Google Sheets
- Google Analytics 4 (GA4)
- Salesforce reports
Data platforms
- Snowflake
- Google BigQuery
- Amazon Redshift
- Databricks
- Data warehousing
- Data governance
Soft skills, and how to prove them
| Skill | What proves it on your resume |
|---|---|
| Business curiosity | An analysis you started because a number looked wrong or a question went unanswered, and what it found. |
| Data storytelling | A presentation or written summary that led a named team to change a plan or budget. |
| Stakeholder management | Recurring requests from sales, finance or operations that you scoped and turned into self-serve reports. |
| Accuracy | Validation checks or reconciliations you added that caught errors before they reached a report. |
| Prioritization | A request queue you handled for several teams, shown by turnaround time or the number of stakeholders served. |
Action verbs for data analyst resumes
- Analyzed
- Queried
- Visualized
- Reconciled
- Segmented
- Forecasted
- Identified
- Presented
- Standardized
- Defined
- Cleaned
- Tracked
- Automated
- Uncovered
What to quantify
- Time saved — analyst or stakeholder hours per week after automating a report
- Money influenced — revenue gained or cost cut from a recommendation, in dollars
- Dashboard adoption — weekly viewers or teams relying on a dashboard
- Data quality — discrepancies resolved, or match rate between two systems
- Scale — rows, tables or data sources combined in an analysis
- Experiment results — conversion lift from an A/B test you analyzed
- Turnaround — days from request to answer
Before and after: data analyst resume bullets
Numbers in [brackets] are placeholders. Fill them in from your own records; never estimate a figure you can’t explain.
- Before
- Created Tableau dashboards for the sales team to replace the weekly Excel report
- After
- Replaced the weekly Excel sales report with [N] Tableau dashboards used by [N] reps, saving [X] analyst hours per week
- Before
- Used SQL to analyze customer data and figure out why people were canceling
- After
- Queried [N] months of customer data in SQL to rank the top [N] cancellation drivers, findings the [team] used to [decision]
- Before
- Cleaned up mismatches between the CRM and billing data
- After
- Reconciled [N] customer records between [CRM name] and the billing system, resolving [N] mismatches
Common data analyst resume mistakes
- Listing Excel and SQL with no sense of depth. Name the techniques you use, such as window functions, CTEs, pivot tables or Power Query.
- Ending bullets at the deliverable ("built dashboards") without the audience or the decision it informed.
- Writing "used data to drive insights" or similar phrases that describe every analyst and prove nothing.
- Adding machine learning terms you haven't applied at work to look like a data scientist. It weakens the match for analyst roles and invites questions you can't answer.
- Publishing confidential figures. If revenue or user counts are sensitive, use percentages or rounded ranges.
What to emphasize at your level
- Entry level
- Show SQL and one BI tool through a portfolio project on a public dataset, and describe internships or coursework by the question you answered, not the tools alone.
- Mid level
- Lead with reporting you own and analyses that changed decisions, each tied to a business metric such as revenue, cost or retention.
- Senior
- Emphasize metric definitions you standardized, data models or self-serve tools you built, and the strategic questions you answered for leadership.
Certifications worth listing
List a certification only if you hold it (or say “in progress” with an expected date).
- Microsoft Certified: Power BI Data Analyst Associate
- Tableau Certified Data Analyst
- Google Data Analytics Professional Certificate
- Microsoft Office Specialist: Excel Expert
Data Analyst resume FAQ
Should a data analyst list Excel on a resume?
Yes. Excel is still requested in many analyst postings, and leaving it off can cost a keyword match. Make it specific: pivot tables, XLOOKUP, Power Query, or models other people rely on. If you mainly work in SQL and BI tools, keep Excel in the skills list but let your bullets show the heavier tools, so the reader sees range without mistaking spreadsheets for your ceiling.
Should a data analyst resume emphasize SQL or Python?
SQL first for most roles, since it is the skill analyst postings screen for most consistently. Add Python or R when you have used them for real work such as automating reports, cleaning messy files or running statistical tests, and show the task in a bullet. If a posting requires Python and you only know SQL, be clear about that instead of stretching the truth.
How do I quantify analysis if I don't know the business impact?
Quantify what you can see directly: hours saved, reports replaced, rows or sources combined, dashboard viewers, turnaround time or errors caught. Then ask the stakeholder what happened after your analysis; a product or finance lead can often tell you the decision or the dollar figure. If nobody knows, describe the decision your work fed into without inventing an outcome.
Do data analysts need a portfolio?
It helps most when you are entering the field or changing industries. Two or three projects on public datasets, each with a clear question, clean SQL or Python and a dashboard, give interviewers something concrete to discuss. Host them on Tableau Public, GitHub or a simple site and link them in your header. Experienced analysts can usually rely on work bullets instead.
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