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COVERPILOT FIELD NOTES · AUGUST 13, 2026 · 6 MIN READ

Resume keywords for data analysts (2026 list)

Analyst postings are unusually keyword-dense: tools, languages, methods and business metrics all in one description. That's good news, because it means the terms a recruiter searches for are sitting right there in the posting. Below are the ones that come up most, grouped so you can check your resume against them quickly.

One rule before the lists: only claim what you've actually used. Analyst interviews are practical. Writing "dbt" because it appeared in a posting gets you a technical screen you can't pass, which is a worse outcome than not getting it.

Languages and querying

SQL is the one that appears in nearly every posting — and be specific about the dialect if you know it: PostgreSQL, MySQL, T-SQL, BigQuery SQL, Snowflake SQL. Beyond that: Python (with pandas, NumPy, scikit-learn if true), R (dplyr, ggplot2, tidyverse), DAX, VBA, and advanced Excel — pivot tables, Power Query, INDEX/MATCH, XLOOKUP. Excel is easy to feel snobbish about and still shows up in a surprising number of screens.

Visualization and BI

Tableau, Power BI, Looker and Looker Studio (say Looker Studio, not the retired "Google Data Studio", unless the posting uses the old name), Qlik, Metabase, Mode, Superset, plus matplotlib, Seaborn or Plotly if you work in Python. Name the specific tool — "data visualization" alone matches nothing a recruiter types.

Warehouses, pipelines and cloud

Snowflake, BigQuery, Redshift, Databricks, dbt, Airflow, ETL and ELT, data modeling, star schema, data warehouse, and the cloud platform involved — AWS, GCP or Azure. Junior analysts often have more of this than they think: if you built a scheduled query that feeds a dashboard, that's a pipeline.

Methods and statistics

A/B testing and experiment design, statistical significance, hypothesis testing, regression analysis, cohort analysis, segmentation, forecasting, time series analysis, predictive modeling, clustering, data cleaning. Note the wording trap here: many teams say "split test" or "experimentation" internally while the posting says "A/B testing". Match the posting.

Business metrics — the ones that separate candidates

This is where most analyst resumes are thin, and where hiring managers actually look. Depending on the industry: churn and retention, LTV, CAC, ARR and MRR, conversion rate, funnel analysis, attribution, KPI definition and reporting, unit economics, margin analysis, demand forecasting, inventory turnover.

An analyst who writes "built a churn model that flagged at-risk accounts 30 days early, cutting monthly churn from 4.1% to 3.2%" is telling a business story. One who writes "used Python and SQL" is listing tools. Both matter, but only the first survives the hiring manager's read.

Certifications and adjacent terms

Google Data Analytics Certificate, Microsoft Certified: Power BI Data Analyst, Tableau Desktop Specialist, AWS Certified Data Analytics, GA4, Google Analytics, Jira, Git, stakeholder management, requirements gathering, data governance, dashboard design.

Where to put them

Two example bullets

Before: "Responsible for building dashboards and reports for the marketing team."

After: "Built a Looker Studio dashboard on BigQuery that replaced 6 hours of weekly manual reporting; adopted by 3 marketing teams within a month."

Before: "Performed data analysis to support business decisions."

After: "Ran cohort and funnel analysis in SQL that identified a checkout drop-off; the fix lifted conversion 11% quarter over quarter."

Check before you send

Every posting weights these differently — a startup analyst role and a bank's reporting role share a title and almost nothing else. Rather than guessing which of these terms matter for the job in front of you, paste your resume and the posting into our free ATS resume checker: it lists which requirements you already cover and which ones you don't mention. Then add only the ones that are honestly yours (the full tailoring method).