Data Analyst

Data Analyst Resume Example

Use this example to learn how to format and structure your data analyst resume for maximum impact.

Reviewed by the AgentCSX teamCareer & resume specialists

Created with AgentCSX

Michael Chen

Data Analyst

New York, NY · m.chen@email.com · (212) 555-0148 · linkedin.com/in/mchen

Professional Summary

Analytical data professional with 5+ years turning complex datasets into decisions. Skilled in SQL, Python, and BI tools, with a record of automating reporting and driving measurable business impact.

Professional Experience

Senior Data AnalystJun 2021 – Present

DataCorp

• Built executive dashboards that informed over $4M in annual budget decisions.
• Developed predictive models that improved demand forecasting accuracy by 18%.
• Partnered with product and finance to define company-wide KPIs.

Data AnalystJan 2019 – May 2021

Analytics Inc

• Automated weekly reporting, saving the team 15+ hours per week.
• Cleaned and modeled data across 10+ sources into a unified warehouse.

Projects

Churn Prediction Model2022

Built a churn model in Python that flagged at-risk accounts with 82% precision, helping retention reduce monthly churn by 12%.

Education

B.S. Statistics2019

New York University

Skills

SQL · Python · Tableau · Power BI · Excel · dbt · Snowflake · Statistics · Data Modeling · A/B Testing

Certifications

Google Data Analytics Professional Certificate2021

How to format a data analyst resume

• Single column, standard headings. Use "Experience," "Projects," "Education," and "Skills" so ATS can parse it correctly.
• Reverse-chronological order. Most recent role first.
• Lead with metrics. Analysts are judged on impact — put numbers in every bullet.
• Real text, not graphics. Avoid charts and tables inside the resume — see our ATS guide.

Resume writing tips

• Quantify the business impact, not just the analysis ("informed $4M in decisions").
• Name your tools. SQL, Python, Tableau, and dbt are common ATS keywords — see resume tailoring.
• Show the full pipeline: collection, cleaning, modeling, and communication.
• Highlight stakeholder work — analysts who influence decisions get promoted.

Common resume mistakes

• Listing tools without showing what you built with them.
• No metrics — "analyzed data" says nothing. Here's why callbacks stall.
• Overloading with every library instead of the ones the job asks for.
• Fancy multi-column templates that break ATS parsing.

Skill requirements

• Query & languages: SQL, Python, R
• BI & viz: Tableau, Power BI, Looker
• Data: dbt, Snowflake, BigQuery, ETL
• Analytics: Statistics, A/B testing, forecasting
• Soft: Stakeholder communication, storytelling with data

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