What skills should a data analyst resume list?

SQL first, then a scripting language such as Python or R, a BI tool such as Power BI, Tableau or Looker, spreadsheet modelling, and statistical methods like A/B testing and regression. Match the exact tool names in the job posting.

Should I add projects to a data analyst resume?

Yes if you are junior or switching careers. Each project needs the business question, the dataset, the method and the finding — a link to a notebook with no explanation adds nothing.

How do I quantify analyst work when the numbers are confidential?

Use percentages, relative improvements, time saved or the scale of data handled. These communicate impact without disclosing internal figures.

Data Analyst Resume Example & Writing Guide

A data analyst resume is judged on whether your analysis changed a decision. Tools get you past the filter; business impact gets you the interview. This example shows how to present SQL, Python and BI work as outcomes rather than a tool inventory.

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Data Analyst resume summary example

Data analyst with 4 years in e-commerce, working across SQL, Python and Looker. Built the retention model that redirected a ₹1.2 crore annual retention budget and cut monthly reporting effort by 30 hours.

Example achievement bullet points

Built a churn-driver analysis that reallocated 40% of the retention budget to the two highest-yield cohorts
Automated 12 recurring reports in dbt and Looker, saving roughly 30 analyst hours per month
Designed the experiment framework used for 60+ A/B tests across three product teams
Reduced dashboard load time from 40 to 4 seconds by rewriting the underlying aggregate tables

Skills to list

Analysis: SQL, Python (pandas, NumPy), statistics, A/B testing, cohort analysis
Visualisation: Looker, Power BI, Tableau, Google Data Studio
Data stack: dbt, BigQuery, Snowflake, Airflow, Excel
Business: forecasting, funnel analysis, pricing, retention modelling

Lead with the decision, not the query

Every analyst writes SQL. Very few describe the decision their analysis changed. The structure that works is: what question the business had, what you analysed, and what changed as a result.

If a number is confidential, use a percentage or a relative measure. 'Increased trial-to-paid conversion 18%' is publishable even when the absolute revenue is not.

State the business metric you moved, then the technique you used
Include data scale where it is impressive — rows, sources, users, markets
Name the stakeholders: marketing, finance, product, operations
Mention automation savings in hours, they read as directly as revenue

Structuring the resume

Analysts benefit from a compact technical skills block near the top, because recruiters filter on tool names, followed by experience written as impact bullets. Add a projects section if you are moving into analytics from another field.

Header with LinkedIn and a portfolio or GitHub link
Summary: domain, years, tools, one flagship result
Technical skills grouped by category
Experience: 3–5 impact bullets per role
Projects: end-to-end analyses with the dataset, method and finding
Education and certifications

Breaking into analytics without a job title

If your title was not 'analyst', pull the analytical part of your old job forward. Operations, finance, support and marketing roles all generate genuine analysis work that counts.

Portfolio projects should use real public datasets and answer a question a business would actually ask. Three deep projects beat ten notebook exercises.

Frequently asked questions

What skills should a data analyst resume list?: SQL first, then a scripting language such as Python or R, a BI tool such as Power BI, Tableau or Looker, spreadsheet modelling, and statistical methods like A/B testing and regression. Match the exact tool names in the job posting.
Should I add projects to a data analyst resume?: Yes if you are junior or switching careers. Each project needs the business question, the dataset, the method and the finding — a link to a notebook with no explanation adds nothing.
How do I quantify analyst work when the numbers are confidential?: Use percentages, relative improvements, time saved or the scale of data handled. These communicate impact without disclosing internal figures.