Data analyst job postings are among the most ATS-heavy in the market. Companies list specific tools, languages, and methodologies — and if your resume doesn't reflect the exact terminology, you're filtered before a human sees you. Here's how to build a resume that passes every layer.
SQL is mentioned in over 80% of data analyst job descriptions. Yet fewer than half of analyst resumes use it prominently in their skills section. Name your tools explicitly — every time.
Technical skills: be specific and prominent
Your skills section should appear early and be grouped by category. Vague descriptions ('experience with data tools') score near zero in ATS. Specific tool names score highly.
- Query languages: SQL (PostgreSQL, MySQL, BigQuery), dbt, Spark SQL
- Programming: Python (pandas, NumPy, matplotlib, scikit-learn), R
- Visualisation: Tableau, Power BI, Looker, Metabase, Google Data Studio
- Cloud & data warehouse: BigQuery, Snowflake, Redshift, AWS, GCP, Azure
- ETL & pipelines: Airflow, dbt, Fivetran, Stitch
- Spreadsheets: Excel (pivot tables, VLOOKUP, Power Query), Google Sheets
How to write achievement bullets as a data analyst
The formula for data analyst bullets: what data or system you worked with + what analysis or solution you built + what business outcome it drove.
- ❌ 'Created dashboards for the sales team'
- ✅ 'Built a real-time sales performance dashboard in Tableau pulling from Snowflake, reducing weekly reporting time by 6 hours and enabling the team to identify a £240k churn risk before renewal'
- ❌ 'Analysed customer data'
- ✅ 'Analysed 18 months of transaction data using Python and SQL to identify high-LTV customer segments; findings informed a targeted campaign that increased repeat purchase rate by 19%'
ATS keywords specific to data analyst roles
Beyond tools, these phrases appear consistently in analyst JDs and should appear in your experience bullets:
- data cleaning, data wrangling, data modelling
- statistical analysis, regression, hypothesis testing, A/B testing
- business intelligence, BI reporting, KPI tracking
- stakeholder presentations, data storytelling, insight generation
- data pipeline, ETL, data governance, data quality
- ad-hoc analysis, self-serve analytics, executive reporting
Projects and portfolio
A GitHub profile or portfolio link showing real analysis work is a meaningful differentiator — particularly for junior analysts. Even one well-documented Jupyter notebook analysing a public dataset demonstrates practical skill better than 10 generic bullet points.
Common mistakes on data analyst resumes
- Listing 'Microsoft Office' as a skill without specifying advanced Excel capabilities
- Describing analysis you ran without mentioning the outcome or decision it informed
- Using generic language like 'data-driven' or 'analytical mindset' that adds no keyword value
- Forgetting to mention the scale of data: rows, tables, frequency of updates matter to hiring managers
Paste your data analyst resume into HireSprint along with the job description. The AI identifies exactly which tools and phrases are missing from your resume and scores your ATS match before you apply.
