Data Analyst postings are written in tools and deliverables: the stack (SQL, Python, BI), the artifacts (dashboards, reports, tests), and the decisions your numbers supported. Generic 'analyzed data' bullets disappear in screening; named tools and measurable outcomes surface.
Keywords that appear in Data Analyst job descriptions
- SQL
- Python
- Power BI
- Tableau
- dashboards
- ETL
- data cleaning
- A/B testing
- cohort analysis
- statistics
- Excel
- data visualization
- KPI
- forecasting
- stakeholder reporting
Use only the keywords that are true for your experience — screening software matches them, but humans read them.
How to rewrite your bullets for a Data Analyst job
Generic
Analyzed sales data and created reports.
Tailored
Built 5 Power BI dashboards on top of SQL models, replacing weekly manual reports and saving the sales team ~6 hours a week.
Generic
Helped with A/B tests.
Tailored
Designed and analyzed 12 A/B tests on the signup funnel; two winning variants lifted activation by 9%.
The numbers above are placeholders — swap in your real ones.
Common Data Analyst resume mistakes
- Jargon without business impact: a page of model names and libraries, but no line about what decision or metric the analysis moved.
- Not mirroring the vacancy's stack word-for-word — writing 'BI tools' when the posting says 'Power BI' loses the keyword match.
- Projects without numbers: 'built dashboards' says nothing; 'built 5 dashboards that replaced weekly manual reports' scans.
Summary line patterns
- Data Analyst (SQL, Python, Power BI) with 3 years in SaaS — dashboards, funnel analysis, and A/B testing that shipped decisions.
- Analyst focused on marketing data: attribution, cohort analysis, and forecasting in a EUR 2M budget environment.
Knockout requirements to check before applying
- Work authorization
- Specific stack listed as required (e.g. 'must have Tableau')
- Minimum years with SQL/Python
- Language level for stakeholder-facing roles