Data Analyst Resume Keywords (Project Keywords + ATS Examples)
Analyst projects should emphasize the business question, the metric definition, and the decision influenced.
Business questions & KPIs
Why the analysis mattered.
- funnel drop-off
- campaign performance
- cohort retention
- unit economics
- forecast accuracy
- pipeline health
- customer segmentation
- operational efficiency
- support volume
- churn drivers
Weak: Analysis project
Strong: Diagnosed activation drop to step 3; quantified impact on ARR from lost conversions.
Weak: KPI work
Strong: Standardized MRR definition across teams ending monthly metric debates.
Data sources & quality
Trust signals.
- warehouse
- events
- CRM
- billing
- surveys
- reconciliation
- data quality
- missing data
- definitions
- grain
Weak: Data
Strong: Joined Salesforce + product events; fixed duplicate user mapping inflating funnel.
Weak: Quality
Strong: Caught tracking gap inflating activation; partnered with eng to patch SDK.
Methods & deliverables
How you worked.
- SQL
- dashboards
- Excel models
- forecasting
- segmentation
- experiment readout
- insight deck
- executive summary
- ad hoc analysis
- self-serve reporting
Weak: Methods
Strong: Built cohort views in SQL powering Looker dashboards for weekly reviews.
Weak: Deliverable
Strong: Executive one-pager influencing Q3 marketing budget shift.
Stakeholders & adoption
Influence.
- presented findings
- weekly business review
- partnered with PM
- finance alignment
- sales enablement
- action owners
- follow-up metrics
- decision log
- adoption tracking
- training
Weak: Stakeholders
Strong: Presented to leadership; three initiatives reprioritized based on analysis.
Weak: Adoption
Strong: Trained PMs on self-serve dashboard; cut ad hoc requests 25%.
Experimentation language
Growth analysts.
- experiment design
- readout
- incrementality
- holdout
- guardrails
- segments
- power
- significance
- rollout decision
- post-launch monitoring
Weak: Experiment
Strong: Supported pricing test with guardrails; recommended pause when support SLA slipped.
Weak: Causal language
Strong: Used geo holdouts to validate marketing lift beyond correlation.
Where to use these keywords (ATS + readability)
Experience bullets
Frame projects as questions answered, not charts built.
Experience bullets
Quantify decisions: budget, roadmap, staffing.
Skills
Tools in skills; project narrative in experience.
Summary
Name vertical (marketing, finance) if projects are domain-specific.
Experience bullets
Academic projects OK early-career—tie methods to job needs.
Common mistakes
- Charts without decisions.
- Vague ‘insights’ without metrics.
- No mention of metric definitions or data quality.
- Ignoring experimentation vocabulary for growth roles.