ATS Resume Checker for Data Analysts
Data analyst resumes are screened by ATS for specific tool proficiency, SQL depth, and business intelligence keywords — before any manager evaluates your insight quality. A resume that says 'analyzed large datasets to support business decisions' is invisible to ATS. One that says 'wrote complex SQL (window functions, CTEs) in BigQuery to analyze cohort retention across 2.3M users, built Tableau dashboard used by 8 exec stakeholders weekly' is packed with keyword signals. Our checker shows you exactly what's missing.
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ATS Optimization Tip
SQL depth keywords are more valuable than SQL breadth on a data analyst resume. Don't just list 'SQL' — list 'SQL (window functions, CTEs, subqueries, JOINS)'. ATS systems for senior analyst roles increasingly filter for specific SQL complexity signals. If you've written complex queries, say so explicitly.
Top ATS Keywords for Data Analyst Resumes
These keywords appear most frequently in Data Analyst job descriptions. Missing any of these on your resume could drop your ATS score significantly — especially if the JD emphasizes them.
Paste your actual resume to see which of these keywords you're missing — and get a full gap analysis against the specific job description you're targeting.
Run My Free ATS Score →Data Analyst Resume ATS — Frequently Asked Questions
What ATS keywords matter most for data analyst resumes?
Data analyst ATS scans look for: SQL (and increasingly SQL complexity signals like window functions, CTEs), Python tools (pandas, NumPy, matplotlib), BI tools (Tableau, Looker, Power BI, Metabase — the exact one in the JD), data warehouses (BigQuery, Snowflake, Redshift, Databricks), Excel/Sheets, and analysis type keywords (cohort analysis, funnel analysis, A/B testing, retention analysis, churn modeling). List all tools individually — never group them as 'BI tools'.
How do I write data analyst bullets that pass ATS and impress hiring managers?
Chain the tool, the data scale, and the business impact. 'Wrote SQL window function queries in Snowflake to calculate 30/60/90-day cohort retention across 1.8M users, built Looker dashboard tracking 6 retention KPIs — findings directly informed a product change that improved D30 retention by 9%.' This hits: SQL, window functions, Snowflake, cohort, retention, Looker, KPI, dashboard as separate keyword signals.
Should data analysts include Python or just SQL for ATS?
Include both if you use both. Python is now mentioned in over 50% of data analyst JDs — if it appears in the JD you're applying to, Python must appear on your resume. List specific libraries: 'Python (pandas, NumPy, matplotlib, seaborn)' is better than just 'Python'. If your Python experience is limited to basic data manipulation, that's still worth listing — ATS scores keyword presence, not depth of experience.
How do data analyst resumes differ for tech vs non-tech companies for ATS?
Tech company data analyst ATS scans weight: BigQuery/Snowflake/Redshift, Python, experimentation (A/B testing), product analytics (funnel, retention, activation), and Looker/Amplitude/Mixpanel. Non-tech company ATS scans weight: Excel, Tableau/Power BI, SQL Server/MySQL, reporting, and business process keywords. If you're applying across both, check whether each JD emphasizes tech-stack tools or business-reporting tools and adjust your keyword emphasis accordingly.
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