ATS Keywords for
Data Analysts

Data analyst postings are screened on a narrow set of very concrete tools, and the gap between candidates is usually not analytical ability but vocabulary. Two analysts can do identical work while one writes "built reporting for the sales team" and the other writes "built Tableau dashboards on a Snowflake warehouse using SQL". Only the second matches. Analyst requisitions also carry a business half — stakeholder work, KPI definition — that technical candidates routinely leave out. Both halves are below.

14 ATS keywords for Data Analyst CVs

What each term signals to the system reading your CV.

  • SQL

    The single most screened term in analytics hiring; its absence disqualifies almost automatically.

  • Tableau / Power BI / Looker

    BI platforms are matched by name, and a company standardised on one rarely accepts another as equivalent.

  • Excel (pivot tables, VLOOKUP)

    Still an explicit requirement outside tech, and the specific function names are searched.

  • Python (pandas)

    Separates analysts who can work past the BI tool from those who cannot.

  • Data visualization

    The phrase used in the responsibilities section of nearly every analyst posting.

  • A/B testing

    Experimentation is a named requirement at product companies and a strong differentiator elsewhere.

  • KPI / metrics definition

    Signals you shape what gets measured rather than only reporting it.

  • ETL

    Appears wherever the analyst is expected to prepare their own data.

  • Data cleaning / wrangling

    The realistic bulk of the job, and standard phrasing in the posting.

  • Statistical analysis

    Screened for the roles that go beyond descriptive reporting into inference.

  • Google Analytics

    A hard requirement for marketing- and ecommerce-facing analyst roles.

  • Dashboard development

    The concrete deliverable recruiters search for when staffing a reporting function.

  • Stakeholder management

    The business half of the job, and often the tiebreaker between two technically equal CVs.

  • Data storytelling

    Increasingly written into requisitions as the skill that makes analysis land with executives.

How to use this list

  • Put SQL in your skills section and demonstrate it in a bullet — the term is screened and then probed in the first interview.
  • Name the BI tool the posting names. Tableau and Power BI experience do not cross-match even though the skill transfers.
  • Frame each project by the decision it changed, not the chart it produced: that is where the stakeholder keywords live honestly.
  • Include the data volume or source systems you worked with; it distinguishes warehouse-scale work from spreadsheet work.

Frequently asked questions

Everything you need to know before you upload.

Do I need Python to get a data analyst role?

Not for every role — plenty of analyst postings are SQL and BI only. But Python appears in a growing share of requisitions, and its absence limits which filters you pass. If you have it, list it; if not, make SQL depth visible instead.

How do I show impact when my output is a dashboard?

Report what changed because of it: hours of manual reporting removed, a decision made, a metric that moved after the team could see it. Impact phrasing also creates a natural home for KPI and stakeholder keywords.

Is a certification worth adding?

Tool certifications (Tableau, Power BI, Google Analytics) match as literal keywords and are listed as preferred in many postings, so they earn their line. They do not substitute for demonstrated SQL.

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