Data Analyst resume

How to write a data analyst resume that passes the ATS

Updated 7 October 2026

Every data analyst post asks for SQL and a BI tool; most also want Python or R, Excel, and the ability to turn analysis into a decision. The resumes that win show both the tooling and the business outcome the analysis produced.

What the ATS and recruiters look for in a data analyst

SQL is non-negotiable and should appear in skills and in bullets. Then the BI tool in the post (Tableau, Power BI, Looker), Python/R, Excel, and statistical terms (A/B testing, regression, forecasting). Hiring managers read for the decision your analysis drove: revenue found, cost saved, churn reduced.

Data Analyst resume keywords

Use the exact spelling the job post uses. Put the post's must-haves first in your skills section and repeat the important ones inside bullets with results.

Hard skills

SQL (joins, window functions, CTEs)
Data visualisation
Dashboard design
Statistical analysis
A/B testing
Data cleaning and ETL
Forecasting
KPI definition
Data modelling
Reporting automation
Stakeholder requirements gathering
Data quality

Tools & platforms

SQL
Excel (pivot tables, Power Query)
Tableau
Power BI
Looker
Python (pandas)
R
Google Analytics
BigQuery / Snowflake
dbt
Jupyter
Git

Soft skills (show in bullets)

Storytelling with data
Business acumen
Stakeholder management
Curiosity
Attention to detail

How to structure each section

Summary. Domain + tools + a result: "Data analyst with 3 years in e-commerce; SQL, Tableau, Python; found $1.2M in pricing leakage."
Skills. Languages & Query, BI & Visualisation, Statistics, Platforms. Put SQL first.
Experience. Every bullet: the question, the analysis, the tool, the decision and its value.
Projects / Certifications. Google Data Analytics, Tableau Desktop Specialist and similar are keyword matches; list them with the issuing body.

Before and after: data analyst bullet points

BeforeCreated reports and dashboards for the sales team.

AfterBuilt a Power BI dashboard over Snowflake sales data (SQL, dbt) that replaced 12 weekly manual reports and surfaced a 9% discounting leak, recovered within one quarter.

Why it works: Names tools, scale and a money outcome; "replaced manual reports" shows efficiency.

BeforeAnalysed customer data to find trends.

AfterSegmented 400k customers in Python (pandas, k-means) and identified a high-churn cohort; the retention campaign targeted at it cut monthly churn from 6.1% to 4.8%.

Why it works: Specific method, specific population, measured business result.

Mistakes that get data analyst resumes filtered out

Listing tools without a single bullet that uses them.
Describing dashboards as the output; the decision is the output.
Omitting SQL from bullets because "it's obvious". The ATS counts occurrences.
Calling yourself "Data Scientist" for an analyst role; mirror the title.

Tailor it to the job in minutes

Resumeni reads the job description, finds the keywords you're missing, and rewrites your resume for the role while keeping your own layout and fonts. The first check-up is free: you see your ATS score, the gaps and a few rewritten lines before paying anything. Or run the free ATS checker on your current resume first.

Frequently asked questions

Excel or Python: which matters more?

Whatever the post says. Most analyst posts still name Excel; Python/R are differentiators. List both if you have them.

How do I show impact if I don't know the money value?

Use time saved, reports automated, decisions changed, or adoption ("used weekly by 30 managers"). Estimates are fine if labelled.

Do certifications help?

They match keywords and reassure career-changers' screeners. They don't replace a bullet showing you used the skill.