How to write a data analyst resume that goes beyond tool lists and demonstrates insight-driven business decisions.
Most data analyst resumes are tool inventories: "Proficient in SQL, Python, Tableau, Excel." The candidates who get interviews show what they did with those tools and what business decision followed.
Analysis + Insight + Business Action + Outcome
"Analyzed 18 months of churn data using SQL and Python -- identified that users who didn't complete onboarding within 3 days churned at 4x the rate; insight drove product team to redesign Day 1 flow, reducing 30-day churn by 22%."
"Built executive dashboard in Tableau tracking 12 key business KPIs -- replaced 4 manual weekly reports, saving 6 hours/week of analyst and ops time."
"Ran A/B test on pricing page (n=42,000 users) -- identified variant with 18% higher conversion; implemented by product team, contributing $380K incremental ARR."
Entry Level: SQL (required), Excel (required), one BI tool (Tableau, Looker, Power BI), basic Python or R.
Mid-Level: Advanced SQL (window functions, CTEs), Python (pandas, scikit-learn basics), statistics (hypothesis testing, regression), dbt.
Senior Analyst: Statistical modeling, experiment design, business case development, stakeholder management.
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Languages: SQL (advanced), Python (pandas, numpy, matplotlib), R
BI Tools: Tableau, Looker, Power BI, Google Data Studio
Databases: PostgreSQL, BigQuery, Snowflake, Redshift
Other: dbt, Airflow, Excel, Git
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data analysis, SQL, Python, A/B testing, business intelligence, dashboard, KPI, ETL, statistical analysis, Tableau, Looker, data visualization, insights, reporting, Snowflake, BigQuery.
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