How ATS filtering works for data analyst roles
Data analyst job descriptions vary significantly by company and seniority — a "data analyst" at a startup looks very different from one at a Fortune 500 retailer. ATS systems filter based on the specific keywords in each job description, which means your keyword strategy needs to be tailored to each application.
The most common filtering mistakes for data analysts:
- Using "Excel" when the job description says "Microsoft Excel" (or vice versa)
- Listing "data visualisation" but not the specific tool (Tableau, Power BI, Looker)
- Missing statistical methodology keywords when the role requires them
- Not including the business domain keywords (e.g., "financial analysis," "marketing analytics," "supply chain analytics")
Use the resume keywords checker to identify exactly which keywords each job description requires that your resume is missing.
Core data analyst resume keywords
Data tools and platforms:
- SQL (Structured Query Language) — specify variants: PostgreSQL, MySQL, BigQuery, Snowflake, Redshift
- Python (with libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn)
- R (if used in academic or statistical roles)
- Microsoft Excel, Advanced Excel, Excel VBA
- Google Sheets
BI and visualisation tools:
- Tableau, Power BI, Looker, Looker Studio (Google Data Studio)
- Metabase, Mode Analytics, Qlik
- Data visualisation, dashboard development, data storytelling
Data concepts and methodology:
- Exploratory Data Analysis (EDA)
- Statistical analysis, descriptive statistics, inferential statistics
- A/B testing, hypothesis testing
- Regression analysis, correlation analysis
- Data cleaning, data wrangling, data transformation
- ETL (Extract, Transform, Load)
- Data modelling
- KPI definition and tracking
Communication and reporting:
- Executive reporting, stakeholder communication
- Data-driven decision making
- Presentations, data storytelling, business insights
Industry-specific data analyst keywords
Different industries look for domain-specific terminology alongside the technical skills:
Marketing analytics:
- Customer acquisition, retention, churn analysis
- Campaign performance analysis, ROI measurement
- Google Analytics, Adobe Analytics, Mixpanel, Amplitude
- Funnel analysis, cohort analysis, attribution modelling
- CRM data (Salesforce, HubSpot)
Finance and FP&A:
- Financial modelling, variance analysis, budget vs. actuals
- P&L analysis, revenue analysis
- DCF, NPV, IRR (for financial analysis roles)
- Excel financial models, pivot tables
Operations and supply chain:
- Demand forecasting, inventory optimisation
- Process efficiency analysis, capacity planning
- Operations Research (OR), logistics data
Product analytics:
- Product metrics, user behaviour analysis
- Retention analysis, engagement metrics
- DAU/MAU, session analysis
- Feature adoption, conversion rate optimisation
How to write keyword-rich achievement bullets for data analysts
Every keyword on your resume should appear in context — not in a skills list alone. Here is how to write achievement bullets that include keywords naturally:
Structure: [Action verb] + [specific tool/method] + [business impact]
Examples:
- "Built automated Tableau dashboards tracking 12 KPIs for the operations team, reducing weekly reporting time by 4 hours"
- "Conducted SQL-based cohort analysis identifying customer segments with 2x higher LTV, informing a $2M marketing reallocation"
- "Designed A/B testing framework in Python (Pandas, SciPy) for product team, improving feature adoption rate by 18%"
- "Cleaned and transformed 50M+ row datasets using dbt and Snowflake, improving query performance by 60%"
Action verbs for data analysts: analysed, built, designed, developed, identified, implemented, modelled, optimised, presented, quantified, reduced, reported, synthesised, transformed, visualised