Data Analyst Resume Keywords (Technical Skills Keywords + ATS Examples)
Analyst resumes should read like a toolkit tied to business questions. Group skills the way finance/product orgs think: definitions, reporting, diagnostics, and experimentation support.
SQL & analytics engineering
Core analyst technical bar.
- SQL
- CTEs
- window functions
- joins
- subqueries
- query optimization
- dbt
- data tests
- documentation
- version control
Weak: Strong SQL
Strong: CTEs for multi-step funnel reconstruction with readable, audited logic.
Weak: dbt
Strong: dbt models with tests catching grain mismatches before leadership reviews.
BI & visualization
How insights are consumed.
- Tableau
- Looker
- Power BI
- LookML
- DAX
- data modeling
- semantic layer
- dashboard design
- storytelling
- self-serve
Weak: Tableau
Strong: Executive dashboards with certified fields and governance-approved definitions.
Weak: BI
Strong: Reduced chart junk; focused KPIs aligned to OKRs.
Statistics & experimentation support
Differentiates strong analysts.
- significance testing
- confidence intervals
- sample size
- A/B testing support
- power analysis
- bias checks
- forecasting
- seasonality
- segmentation
- propensity
Weak: Statistics
Strong: Built CIs for campaign lift estimates used in budget decisions.
Weak: Experiments
Strong: Partnered on experiment spec—metrics, segments, and guardrails.
Warehouse & pipeline awareness
Modern analyst expectations.
- Snowflake
- BigQuery
- Redshift
- ETL
- Airflow
- Fivetran
- data quality
- DQ tests
- lineage
- scheduling
Weak: Snowflake
Strong: Debugged join explosions affecting KPI; fixed upstream grain issue.
Weak: Pipelines
Strong: Collaborated with eng on event schema for cleaner funnel metrics.
Productivity & collaboration tools
Often in JDs.
- Excel
- Google Sheets
- Notion
- Confluence
- Jira
- Git
- Python
- pandas
- APIs
- CSV tooling
Weak: Excel / Sheets
Strong: Financial models for scenario planning used by FP&A.
Weak: Python
Strong: pandas for ad hoc analyses too heavy for SQL-only workflows.
Where to use these keywords (ATS + readability)
Skills
Lead with SQL + primary BI tool from the posting.
Experience bullets
Tie skills to decisions: ‘SQL + Looker → leadership reallocated budget’.
Skills
Include experimentation stats terms when role owns test readouts.
Summary
Mention domains: marketing, finance, product—skills gain meaning with domain.
Experience bullets
Reference semantic layers/metric ownership when you governed definitions.
Common mistakes
- Calling yourself ‘advanced SQL’ with only simple selects.
- BI tool listed without dashboard ownership examples.
- No stats/experiment language for growth analyst roles.
- Tool dump without business outcomes.