Resume example · updated 2026-07-21
Data Analyst
resume example.
A data analyst resume should explain who used the analysis and what decision changed. Queries and dashboards are outputs; the stronger story covers data quality, analytical method, stakeholder question, and measurable operating effect.
The example claims below are fictional teaching material. Copy the structure, never the facts. Use only evidence you can defend in an interview.
YOUR NAME
Data Analyst · City · email@example.com
Professional summary
Data analyst with five years of experience supporting product and operations teams with SQL, Python, and BI reporting. Built trusted metric definitions, automated recurring analysis, and translated ambiguous questions into decisions teams could act on. Comfortable owning work from source validation through stakeholder readout.
Selected evidence
- Automated weekly operations reporting with SQL and dbt, removing 18 hours of spreadsheet work and giving 14 managers one reconciled metric set.
- Found that a mobile onboarding drop was concentrated in one verification step; the resulting product fix raised completion from 58% to 72%.
- Defined revenue, activation, and retention metrics with finance and product, resolving six conflicting dashboard definitions before quarterly planning.
illustrative specimen · replace every claim
01
Role-specific summary example.
“Data analyst with five years of experience supporting product and operations teams with SQL, Python, and BI reporting. Built trusted metric definitions, automated recurring analysis, and translated ambiguous questions into decisions teams could act on. Comfortable owning work from source validation through stakeholder readout.”
This works because it names role context, strongest scope, and the kind of work the candidate wants next. Keep yours to two or three sentences; remove adjectives that the experience section cannot prove.
02
Skills grouped for a fast scan.
Analysis
- SQL
- Python
- Statistical analysis
- Experiment analysis
- Forecasting
Data systems
- PostgreSQL
- BigQuery
- dbt
- Data quality
- Metric governance
Communication
- Tableau
- Power BI
- Looker
- Data storytelling
- Stakeholder discovery
03
Four bullet examples with annotations.
The numbers are fictional examples. Replace them with your own verified scope or choose a truthful non-numeric outcome.
- 1.
Automated weekly operations reporting with SQL and dbt, removing 18 hours of spreadsheet work and giving 14 managers one reconciled metric set.
Why it works: Names the stack, time saved, audience, and trust improvement.
- 2.
Found that a mobile onboarding drop was concentrated in one verification step; the resulting product fix raised completion from 58% to 72%.
Why it works: Shows analysis leading to a product decision while keeping implementation credit appropriately shared.
- 3.
Defined revenue, activation, and retention metrics with finance and product, resolving six conflicting dashboard definitions before quarterly planning.
Why it works: Makes governance work concrete and shows why it mattered.
- 4.
Built anomaly checks across 24 source tables and cut time to detect broken daily loads from several hours to under 15 minutes.
Why it works: Demonstrates data reliability ownership with a clear before and after.
04
A practical section order.
- 01
Professional summary
- 02
Technical skills
- 03
Experience
- 04
Selected analytical projects
- 05
Education
ATS checklist for this role.
- State SQL dialects and BI tools accurately, but prioritize analytical methods and business context from the posting.
- Name the decision or team served by each dashboard instead of listing dashboard counts alone.
- Include portfolio links only when the work is anonymized, permitted, and understandable without company context.
Common mistakes to remove.
- Writing only about queries, dashboards, and tools with no decision or user.
- Calling descriptive reporting machine learning to match a keyword.
- Sharing confidential company data or screenshots in a public portfolio.
Use your evidence, not the specimen