A template for analysts who turn raw data into business decisions.
Data Analyst with 3 years of experience transforming complex datasets into actionable business intelligence. Proficient in Python, SQL, and Tableau with a background in statistical modelling and A/B testing.
Data Analyst CVs must show that you can extract insights that matter. Recruiters want to see specific tools (Python, SQL, Tableau), the types of analysis you have performed (forecasting, A/B testing, cohort analysis), and the business impact of your work. Did your recommendation save money? Did your dashboard change how leadership makes decisions? Those are the stories that get interviews.
Python (pandas, scikit-learn), SQL, Tableau or Power BI, statistical modelling, A/B testing, Excel, BigQuery or Snowflake, and data visualisation. If you have experience with machine learning or cloud data tools, include those — they are increasingly expected even for analyst roles.
Listing tools without context is the most common mistake. "Proficient in Python" is meaningless without "Built Python-based demand forecasting model reducing stock waste by £2.4M annually." Always pair the tool with the result. Another mistake is burying technical projects under generic bullet points. Lead with your most impactful analysis.
Data roles attract hundreds of applicants. Make your CV scannable with clear sections and bold keywords. Include a "Technical Skills" or "Tools" section near the top so recruiters can quickly verify you meet the baseline requirements.
Use this template or start from scratch — our AI builder will guide you.