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ATS Keywords for Data Analyst Resumes: What to Include and Where

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Data analyst job ads use a fairly small vocabulary: SQL, a BI tool, Excel, sometimes Python, and a lot of business words like KPIs, reporting and stakeholders. Recruiters search for those exact terms. If your resume says "built dashboards" and the posting says "Power BI", you may not come up in the search, even if Power BI is what you used.

Tools and languages

These are searched by name, so write the exact product name.

SQL

The most important term for almost every analyst role. Add the database or warehouse when you know it: PostgreSQL, MySQL, SQL Server, BigQuery, Snowflake, Redshift. If you've written window functions, CTEs or query optimization, those terms show up in senior postings.

BI and visualization

Name the tool: Tableau, Power BI, Looker, Looker Studio, Qlik, Metabase. "Dashboards" alone is too vague for a search. For Power BI roles, DAX and Power Query come up often.

Excel and spreadsheets

Still in most analyst postings. Useful specifics: pivot tables, XLOOKUP or VLOOKUP, Power Query, macros or VBA, Google Sheets.

Python or R

Expected in many roles, required in fewer. If you use Python, name the libraries: pandas, NumPy, matplotlib, seaborn, Jupyter. For R: tidyverse, ggplot2.

Data tools

More common in analytics engineering and product analytics roles: dbt, Airflow, Git, ETL, data modeling.

Product analytics tools

For product and marketing analysts: Google Analytics (GA4), Amplitude, Mixpanel, Adobe Analytics, Segment.

Methods

Statistics

Descriptive statistics, regression, hypothesis testing, statistical significance, correlation.

Experimentation

A/B testing, experiment design, control and treatment groups. Product analyst postings ask for this often.

Analysis types

Cohort analysis, funnel analysis, retention analysis, segmentation, forecasting, root cause analysis, ad hoc analysis.

Data work

Data cleaning, data validation, data quality, data wrangling, exploratory data analysis (EDA).

Business words

Analyst postings use business language more than most technical jobs, and recruiters search for it.

KPIs, metrics, reporting, dashboards, business intelligence, stakeholder management, requirements gathering, data storytelling, data-driven decisions, insights, presenting to leadership.

These words carry less weight alone than tool names. They work best inside a bullet that shows the result: "Defined 12 KPIs with the sales team" says more than "stakeholder management".

Keywords by type of analyst role

The title in the posting tells you which group to put first.

Role Lead with
Data Analyst SQL, Excel, Tableau or Power BI, reporting, KPIs, data cleaning
Business Intelligence (BI) Analyst Power BI or Tableau, DAX, data modeling, data warehouse, ETL
Product Analyst SQL, A/B testing, funnel and cohort analysis, Amplitude or Mixpanel, Python
Marketing Analyst GA4, attribution, campaign performance, ROI, SQL, Looker Studio
Financial Analyst Excel, financial modeling, forecasting, budgeting, variance analysis
Analytics Engineer SQL, dbt, data modeling, Git, Snowflake or BigQuery, Airflow

Spell out abbreviations once

Keyword search is often literal. "BI" and "business intelligence" can count as different terms, and so can "EDA" and "exploratory data analysis". Write the full form once with the abbreviation in brackets, then use whichever form the posting uses.

Watch product names too. "PowerBI" and "Power BI" may not match in a simple search. Copy the spelling from the job ad.

Put the keywords into bullets

A skills section is where a recruiter checks that the terms are there. The bullets are where they believe them. Each important tool should appear in at least one bullet that says what you did with it.

Before:

Created reports and dashboards for the business.

After:

Built a Power BI sales dashboard on SQL Server data, used weekly by 30 regional managers, replacing 6 manual Excel reports.

The second version names two tools, says who used it, and has numbers. A few more patterns:

Wrote SQL in BigQuery to build a customer cohort model that showed 90-day retention by acquisition channel.

Ran A/B tests on the checkout page with the product team; one change lifted conversion by 4%.

Automated a monthly finance report with Python (pandas) and Airflow, cutting preparation time from 2 days to 2 hours.

Use your own numbers. If you don't have an exact figure, describe the scale instead: how many users, reports, stores or people relied on it.

A skills section that works

Group skills so a recruiter can scan them in a few seconds:

  • SQL and databases: PostgreSQL, BigQuery, Snowflake
  • BI and visualization: Tableau, Power BI (DAX), Looker Studio
  • Programming: Python (pandas, NumPy), R
  • Spreadsheets: Excel (pivot tables, Power Query), Google Sheets
  • Methods: A/B testing, cohort analysis, regression, forecasting

Keep it to tools you could talk about for five minutes in an interview. Analyst interviews often include a live SQL or Excel task, and a tool you listed but can't use will come up.

Find the keywords you're missing

The quickest way is to compare your resume with a real posting. Paste the job description and upload your resume to our free ATS resume checker:

  • Keywords from the job shows which terms from the posting are in your resume and which are missing. It recognizes common variants, so "PowerBI" and "Power BI", or "EDA" and "exploratory data analysis", count as one term.
  • Does your resume prove it? checks each requirement against your resume and shows the line that supports it, or marks it as missing.
  • What the ATS sees shows the text extracted from your file, so you can confirm a template hasn't scrambled it.

No account needed. Add the missing terms that are true for you, then check again.

For data science and machine learning roles, see ATS keywords for data scientists and ML engineers.

Quick answers

What are the most important keywords for a data analyst resume?
SQL, Excel, a BI tool (Tableau or Power BI), and Python if the posting asks for it. Then the business terms from the job: KPIs, reporting, dashboards, stakeholders.

Should I list Python if I only use it a little?
Only if you can explain what you did with it. Say how you used it in a bullet, for example "automated a weekly report with pandas".

Do I need both Tableau and Power BI?
No. List the one you've used. Many employers accept either, and the skills transfer. Put the one named in the posting first if you know both.

How many keywords should a data analyst resume have?
There's no target number. Cover the tools and methods named in the posting that you actually have, and show each important one in a bullet.

Is "data visualization" a good keyword?
It's useful, but name the tool as well. Recruiters search for "Tableau" or "Power BI" more often than the general term.

Before your next application, check which keywords your resume is missing. It's free and takes a few seconds.

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