Data Analyst Resume Keywords (Core Keywords + ATS Examples)
Hiring teams look for analysts who can own definitions, ship trustworthy reporting, and drive decisions—not only pull queries. These clusters align with how ATS maps analyst postings to resumes.
SQL, data modeling & BI
Foundation for almost every analyst role.
- SQL
- CTEs
- window functions
- joins
- dbt
- data modeling
- star schema
- Looker
- Tableau
- Power BI
Weak: SQL skills
Strong: Wrote SQL with CTEs and window functions to build cohort retention views used in weekly reviews.
Weak: Dashboards
Strong: Maintained executive KPI dashboards in Looker with certified metrics and row-level security.
Metrics, funnels & business context
Shows you connect data work to outcomes.
- KPIs
- OKRs
- funnel analysis
- conversion rate
- retention
- churn
- cohort analysis
- segmentation
- ROI
- forecasting
Weak: Analyzed data
Strong: Diagnosed activation drop using funnel and segment cuts; recommended UX tests that recovered 8% lift.
Weak: Reporting
Strong: Standardized MRR/churn definitions across finance and ops to end metric debates.
Experimentation & causal language
Increasingly required even outside pure DS roles.
- A/B testing
- experiment results
- holdout groups
- incrementality
- significance
- confidence intervals
- sample size
- guardrail metrics
- post-stratification
- bias checks
Weak: Ran tests
Strong: Supported A/B tests on pricing page with pre-registered metrics and week-over-week readouts.
Weak: Correlation
Strong: Separated correlation from impact using holdouts and geo-based controls.
Data quality, governance & ops
Signals maturity for larger orgs.
- data quality
- anomaly detection
- SLAs
- data dictionaries
- lineage
- ETL
- Airflow
- Snowflake
- BigQuery
- Redshift
Weak: Cleaned data
Strong: Partnered with eng to fix tracking gaps; raised event coverage from 72% to 96% on core flows.
Weak: Pipelines
Strong: Documented semantic layer in BI to reduce duplicate dashboards across teams.
Stakeholder enablement & communication
Analysts win on influence and clarity.
- requirements gathering
- executive summaries
- slide decks
- workshops
- ad hoc analysis
- narratives
- prioritization
- stakeholder management
- cross-functional
- self-serve analytics
Weak: Communication skills
Strong: Translated funnel findings into a 3-slide exec brief that unlocked roadmap reprioritization.
Weak: Meetings
Strong: Facilitated weekly metrics review with PM/marketing with action owners and deadlines.
Where to use these keywords (ATS + readability)
Summary
Highlight domains (marketing, product, finance) and decision types you support.
Example: Analyst partnering with growth and product on funnel diagnostics, experimentation readouts, and KPI reporting.
Skills
List BI + warehouse stack explicitly if the posting names them.
Experience bullets
Quantify decisions influenced: budget shifts, roadmap bets, support volume avoided.
Example: Recommended channel mix change that improved ROAS 17% QoQ after geo holdout analysis.
Experience bullets
Name the audience: leadership, PM, finance—matches stakeholder-heavy JD language.
Skills
Include ‘metric definitions’ skills when you owned semantic layers or finance alignment.
Common mistakes
- Vague ‘data-driven’ claims without metrics, definitions, or decisions.
- Keyword dumping vendor names without how you used them in production reporting.
- Ignoring experimentation terms when the role owns test readouts.
- Burying SQL depth—many ATS templates weight SQL strongly for analysts.