Data Analyst resume example & keywords
A data analyst resume should prove you turn raw data into decisions: SQL and a BI tool up top, then bullets where analysis changed an outcome — revenue found, churn reduced, hours saved. Mirror the posting's tools exactly (Tableau vs Power BI) and quantify every claim.
What skills should a data analyst resume include?
Hard skills (the keyword layer — mirror the posting's exact wording where true of you):
- SQL
- Excel (advanced)
- Python (pandas)
- Tableau
- Power BI
- Data cleaning / ETL
- Statistics & A/B testing
- Google Analytics 4
- Data modeling
- Dashboard design
Soft skills — shown through bullets, not listed as adjectives:
- Stakeholder communication
- Problem framing
- Data storytelling
- Prioritization
ATS keywords for data analyst roles
Terms recruiters search and applicant tracking systems rank on for this title — work the true ones into your bullets and skills section (see how ATS screening works):
- data visualization
- business intelligence
- KPI reporting
- data pipeline
- exploratory analysis
- forecasting
- cohort analysis
- data quality
- requirements gathering
- automation
- insights
- SQL queries
Example resume bullet points
Quantified patterns to adapt to your own numbers — never copy claims that aren't yours:
- Built a churn-risk dashboard in Tableau used by 3 teams; flagged accounts received outreach that cut quarterly churn 12%.
- Wrote SQL models consolidating 6 source systems into one reporting layer, eliminating ~10 hours/week of manual Excel work.
- Designed and analyzed an A/B test on onboarding flow; winning variant lifted activation 9 points.
- Automated weekly KPI reporting with Python, reducing turnaround from 2 days to 30 minutes.
- Identified a pricing data error worth ₹40L/yr in misbilled accounts during a revenue audit.
- Partnered with product managers to define 12 core metrics, ending dashboard sprawl across 40+ conflicting reports.
What do recruiters look for in a data analyst resume?
Hiring managers look for the tool overlap first — SQL is assumed, the BI tool must match theirs — then for evidence you influenced a decision, not just produced charts. 'Built dashboard' is table stakes; 'dashboard that changed X' is the interview trigger. Domain familiarity (SaaS, retail, banking) often breaks ties.
Tips that move interviews
- Every bullet needs a number: rows processed, hours saved, metric moved.
- Match the BI tool in the posting exactly; list the other one second if you know both.
- Link a portfolio or public dashboard if you have one — analysts who show work convert better.
Pay differs sharply by industry and city; consult Glassdoor, AmbitionBox (India), or recruiter salary guides for current local ranges.
Frequently asked questions
Do data analysts need Python on the resume?
Increasingly yes for mid-level roles — pandas for cleaning and automation is the common bar. Entry roles often clear on SQL + Excel + one BI tool. Listing Python you can't interview on backfires; mark proficiency honestly.
What's the difference between a data analyst and data scientist resume?
Analyst resumes emphasize SQL, BI tooling, and decision impact; data scientist resumes add modeling, ML libraries, and experiment design depth. Applying across both titles means re-tailoring keywords — the postings are screened differently.
How do freshers get data analyst interviews without experience?
Projects that mimic the job: pick a public dataset, define a business question, publish the SQL/notebook and a short write-up with a recommendation. Two or three of these, quantified, outperform a coursework list.