Table of Contents
Data analyst roles span every industry - e-commerce, banking, healthcare, logistics, and product companies. The core skills remain consistent: extract data (SQL), process and analyse it (Python or Excel), and communicate findings (Tableau, Power BI, or dashboards). A good data analyst resume shows you can do all three with real evidence.
#What Recruiters Look for in a Data Analyst Resume
- SQL proficiency - this is non-negotiable for almost every data analyst role
- Python or R for data wrangling and analysis (pandas, NumPy)
- Data visualisation - Tableau, Power BI, or Python (matplotlib, seaborn)
- Excel / Google Sheets for stakeholder-facing analysis
- Statistical reasoning - hypothesis testing, regression, correlation
- Communication of insights - not just analysis, but actionable recommendations
- Domain knowledge (e-commerce, finance, healthcare, depending on the role)
#Professional Summary Examples
Summary - Fresher
"Data and analytics graduate with hands-on experience in SQL, Python (pandas), and Tableau through three academic and personal projects. Comfortable with hypothesis testing, cohort analysis, and building dashboards. Seeking a data analyst role where I can turn messy data into clear business decisions."
Summary - Experienced (2 years)
"Data analyst with 2 years of experience at a D2C e-commerce company, supporting growth, retention, and product teams with SQL-based analysis and Power BI dashboards. Delivered a customer churn model that saved ₹18L in annual customer acquisition cost. Looking for analyst roles with more exposure to predictive analytics."
#Technical Skills for Data Analysts
Skills Section
"Languages & Tools: SQL (MySQL, PostgreSQL, BigQuery), Python (pandas, NumPy, matplotlib, seaborn), R (basic) Visualization: Tableau, Power BI, Google Looker Studio Spreadsheets: Excel (VLOOKUP, Pivot Tables, Power Query), Google Sheets Statistics: Hypothesis Testing, A/B Testing, Regression Analysis, Cohort Analysis Other: Git, Jupyter Notebooks, Jira, Confluence"
#Experience Bullet Examples
Experience Bullets
"Analysed sales data and created reports."
"Analysed 14 months of sales data using SQL and Python to identify seasonal demand patterns, enabling the merchandising team to reduce overstock by 23% over Q3 and Q4."
Why this works: Specific data scope, specific tools, specific outcome for a specific team.
Experience Bullets
"Built Power BI dashboards."
"Built 3 real-time Power BI dashboards for customer support, logistics, and marketing teams, replacing 6 manual Excel reports and saving ~18 hours of analyst time per week."
Why this works: How many dashboards, for whom, what it replaced, and how much time it saved.
Experience Bullets
"Helped with A/B testing."
"Designed and analysed A/B test for a new checkout flow (n=12,000 users), finding 2.3% improvement in conversion rate (p<0.01) - led to full rollout and estimated ₹6L additional monthly revenue."
Why this works: Sample size, statistical significance, business impact - this is what an experienced data analyst sounds like.
#Projects for Fresher Data Analysts
Project Entry
"Customer Churn Analysis | Python, SQL, Tableau • Analysed a 50,000-row telecom dataset to identify churn drivers using Python (pandas, seaborn) and logistic regression • Found that customers on month-to-month contracts with no tech support add-on had 3.4x higher churn rate • Built an interactive Tableau dashboard showing churn by contract type, tenure, and service package • Recommended targeted retention offer for high-risk segment; presented findings to a mock business stakeholder panel"
#ATS Keywords for Data Analyst Roles
- SQL / MySQL / PostgreSQL / BigQuery
- Python / pandas / NumPy
- Tableau / Power BI
- Data visualisation
- A/B testing
- Statistical analysis
- Dashboard
- ETL
- Business intelligence (BI)
- KPI / metrics
- Excel / Pivot Tables
- Data cleaning / data wrangling
- Cohort analysis
- Predictive analytics
#Common Data Analyst Resume Mistakes
- Listing SQL with no evidence of query complexity - mention JOIN, subquery, window functions if you have used them.
- No mention of business context - "analysed data" is not enough; what decision did your analysis support?
- Visualisation tools not listed - almost every data analyst role requires this.
- Claiming ML skills without evidence - if you only know basic logistic regression, say that. If you have done NLP or time-series forecasting, say that specifically.
- Projects with no conclusions - show what insight you found, not just that you ran analysis.
Put these tips into action with SmartCampusBuddy
Create a recruiter approved, ATS friendly resume in 10 minutes. Real time ATS scoring, professional templates, and unlimited customization.