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Data Analyst — CV Example

A template for analysts who turn raw data into business decisions.

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What Does a Data Analyst Actually Do?

Data analysts turn raw business data into insights that help teams make better decisions. A typical week includes writing SQL queries, building dashboards in Tableau or Power BI, running ad hoc analyses for stakeholders, and presenting findings to non-technical managers. They work in finance, retail, healthcare, marketing, logistics — any sector that collects data, which is all of them. Most data analysts report to a Head of Data, Analytics Manager, or sometimes directly to a business unit lead like the Head of Marketing or Finance Director.

Amara Okafor
Data Analyst
📍 Bristol, UK✉️ amara.okafor@email.com
Summary

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.

Work Experience
Data Analyst at TescoJan 2022 — Present
  • Built Python-based demand forecasting model reducing stock waste by £2.4M annually across 150 stores
  • Designed 15 Tableau dashboards used by senior leadership to monitor KPIs across 6 business units
Junior Data Analyst at Avon & Somerset PoliceSep 2020 — Jan 2022
  • Analysed crime pattern datasets using Python (pandas, scikit-learn) to support resource allocation decisions
  • Created geospatial visualisations reducing manual reporting workload by 30%
Skills
PythonSQLTableaupandasscikit-learnStatistical ModellingA/B TestingExcel / Power BIBigQuery

What Recruiters Look For

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.

Key Skills to Include

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.

Common Mistakes

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.

Formatting Tips

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.

Average Salary — Data Analyst

United States
$75,000 – $110,000
United Kingdom
$45,000 – $68,000
Germany
$48,000 – $70,000
UAE / Dubai
$50,000 – $75,000
Canada
$65,000 – $90,000
Australia
$70,000 – $100,000

Figures in USD. Ranges reflect mid-level experience (3–7 years). Senior roles and major metro areas typically sit at the top of these bands.

Top 5 Interview Questions — Data Analyst

1Tell me about an analysis you ran that changed a business decision.
Be specific about the question you were trying to answer, the data you used, the method, and what decision the business made as a result. "We increased revenue by 12%" beats "the team found it useful".
2How do you handle a stakeholder who keeps changing what they want from a report?
Show that you ask clarifying questions upfront to understand the actual decision being made, not just the data request. Describe how you document requirements before building anything.
3Write a SQL query to find the top 5 customers by revenue in the last 90 days.
Think out loud. Cover the table structure you would assume, the GROUP BY and ORDER BY logic, and mention edge cases like NULL values or duplicate transactions. Interviewers want to see how you think, not just whether you know syntax.
4How do you validate that your data is clean before presenting findings?
Talk about row counts, NULL checks, duplicate detection, and range validation. Name specific tools — dbt, Great Expectations, or even manual spot-checks. Show that data quality is a habit, not an afterthought.
5How do you make a complex analysis understandable to someone who is not technical?
Describe how you choose the right chart type, lead with the headline insight rather than methodology, and keep dashboards focused on one question. Mention a specific time you simplified something for an executive audience.

How to Tailor Your CV

Amazon and Meta use structured interviews with SQL and statistics tests — practice window functions and probability basics. Consulting firms like McKinsey or Deloitte Analytics want strong storytelling skills alongside technical ability, so your CV needs to show business impact, not just tools used. Retailers like Tesco or ASOS want analysts who understand customer behaviour metrics — cohort analysis, churn, basket size. Financial services firms like JPMorgan or Barclays want rigour and attention to regulatory data standards.

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