Data Analyst CV Guide UK

A strong Data Analyst CV is not just a list of tools. It should show that you can work with data, solve business problems, build reports, explain insights, and support better decisions.

Many beginners make the mistake of writing a generic CV with phrases such as "hardworking", "quick learner", and "completed a data analytics course". These statements are not enough on their own.

A good Data Analyst CV should quickly answer three questions:

• Can you use the right tools?
• Can you apply those tools to real business problems?
• Can you explain your work clearly and professionally?

This guide explains how to write a Data Analyst CV for UK roles, including CV structure, skills, project examples, profile summaries, bullet points, ATS keywords, and common mistakes to avoid.

What Makes a Good Data Analyst CV?

A good Data Analyst CV is clear, focused, and evidence-based.

It should show:

• Your data analytics skills
• Your tools and technical ability
• Your practical projects
• Your business understanding
• Your ability to clean and analyse data
• Your dashboard or reporting experience
• Your communication skills
• Your readiness for entry-level or junior roles

Employers do not expect beginners to know everything. However, they do expect to see evidence that you have practised the core skills required for the role.

Recommended UK Data Analyst CV Structure

For most UK Data Analyst roles, your CV should be one to two pages.

A strong structure is:

• Contact details
• Professional profile
• Technical skills
• Portfolio projects
• Work experience
• Education and certifications
• Additional achievements or interests, if relevant

If you are a beginner or career switcher, place your projects and technical skills near the top. This helps employers see your data ability quickly.

1. Contact Details

Your contact section should be simple and professional.

Include:

• Full name
• Phone number
• Professional email address
• City or region
• LinkedIn profile
• Portfolio or GitHub link, if available

You do not need to include:

• Date of birth
• Marital status
• Full home address
• Photo
• National insurance number
• Personal details unrelated to the job

Example

Name: Aisha Khan

Location: Birmingham, UK

Email: aisha.khan@email.com

Phone: 07XXXXXXXXX

LinkedIn: linkedin.com/in/aishakhan

Portfolio: aishakhananalytics.com

2. Professional Profile

Your professional profile is the short summary at the top of your CV. It should be specific to data analytics.

Avoid generic statements such as:

"I am a hardworking and motivated individual looking for an opportunity."

Instead, write a profile that clearly shows your skills, direction, and value.

Beginner Data Analyst profile example

Aspiring Data Analyst with practical experience in Excel, SQL, Power BI, data cleaning, and dashboard creation. Completed business-focused analytics projects covering sales performance, HR analytics, and customer insights. Confident in preparing clean datasets, analysing trends, building KPI dashboards, and explaining findings clearly to support decision-making.

Career switcher profile example

Career switcher with a background in operations and growing expertise in data analytics. Skilled in Excel, SQL, Power BI, data cleaning, reporting, and business performance analysis. Experienced in working with operational information, identifying process issues, and presenting practical recommendations. Currently building a portfolio of analytics projects focused on business dashboards and decision support.

Graduate profile example

Recent graduate with strong interest in data analytics, business intelligence, and data-driven decision-making. Skilled in Excel, SQL, Power BI, and basic Python for data analysis. Developed portfolio projects involving data cleaning, KPI reporting, dashboard design, and trend analysis. Looking to apply analytical thinking and technical skills in an entry-level Data Analyst role.

Working professional profile example

Business professional with experience in reporting, performance tracking, and stakeholder communication. Skilled in Excel, SQL, Power BI, and data analysis, with practical project experience in sales dashboards, operations reporting, and KPI analysis. Able to convert raw data into clear reports and insights that support business decisions.

3. Technical Skills Section

Your skills section should be easy to scan. Do not write every tool you have ever heard of. Include tools you can explain in an interview.

Recommended Data Analyst skills section

• Excel: Pivot tables, XLOOKUP, IF functions, charts, data cleaning, conditional formatting
• SQL: SELECT, WHERE, GROUP BY, JOIN, HAVING, CASE WHEN, subqueries
• Power BI: Data modelling, Power Query, DAX basics, dashboard design, slicers, KPI cards
• Data Cleaning: Missing values, duplicates, inconsistent formats, validation checks
• Analytics: KPI analysis, trend analysis, percentage change, segmentation, reporting
• Python: Pandas, data cleaning, basic analysis, CSV and Excel handling
• Communication: Data storytelling, dashboard explanation, stakeholder reporting

Better formatting example

Technical Skills

• Data Analysis: Data cleaning, KPI reporting, trend analysis, segmentation, business insights
• Excel: Pivot tables, XLOOKUP, IF formulas, charts, conditional formatting
• SQL: Joins, aggregations, filtering, grouping, CASE WHEN, subqueries
• Power BI: Dashboard design, Power Query, DAX basics, data modelling
• Python: Pandas, NumPy basics, CSV cleaning, basic automation
• Business Skills: Reporting, presentation, problem-solving, stakeholder communication

4. Portfolio Projects

For beginners and career switchers, projects are one of the most important parts of the CV.

A project shows that you can apply your learning to a realistic business problem.

Each project should include:

• Project title
• Tools used
• Business problem
• What you did
• Result or insight

Project example 1: Sales Performance Dashboard

Tools used: Excel, SQL, Power BI

Built a sales performance dashboard to analyse revenue, monthly trends, regional performance, and top-selling products. Cleaned raw sales data, used SQL to summarise revenue by product and region, and created Power BI visuals including KPI cards, trend charts, and category comparisons. Identified key revenue-driving products and highlighted regions requiring management attention.

Project example 2: HR Analytics Dashboard

Tools used: Excel, Power BI

Created an HR analytics dashboard to analyse employee attrition, department-wise headcount, salary trends, and performance distribution. Cleaned employee records, created calculated metrics, and designed a dashboard to help HR teams identify workforce patterns and retention risks.

Project example 3: Customer Behaviour Analysis

Tools used: SQL, Excel, Power BI

Analysed customer purchase behaviour using order and customer data. Used SQL joins and aggregations to calculate repeat purchases, average order value, and customer segments. Built a dashboard showing customer activity, high-value customers, and inactive customer groups.

Project example 4: Marketing Campaign Analysis

Tools used: Excel, SQL, Power BI

Analysed marketing campaign performance by comparing clicks, conversions, cost, revenue, and return on marketing spend. Prepared clean campaign data, calculated performance metrics, and created a dashboard to identify the strongest performing channels and underperforming campaigns.

Project example 5: Data Cleaning Project

Tools used: Excel, Python

Cleaned a messy dataset containing missing values, duplicate records, inconsistent date formats, and spelling variations. Standardised columns, removed duplicates, corrected formats, and prepared a clean dataset for reporting. Documented the cleaning process and validation checks.

5. Work Experience

Your work experience does not need to be from a Data Analyst job to be useful.

If you have worked in customer service, sales, operations, finance, admin, HR, marketing, teaching, healthcare, retail, or logistics, you may already have transferable experience.

The key is to rewrite your experience in a data-focused way.

Weak bullet point

Responsible for preparing reports.

Strong bullet point

Prepared weekly performance reports using Excel, summarising key metrics and highlighting trends for management review.

Weak bullet point

Worked with customer records.

Strong bullet point

Maintained and updated customer records, ensuring accurate information for reporting, communication, and follow-up activities.

Weak bullet point

Helped the team with sales data.

Strong bullet point

Supported sales tracking by organising spreadsheet data, calculating monthly totals, and identifying high-performing products and regions.

Weak bullet point

Used Excel at work.

Strong bullet point

Used Excel to clean data, create formulas, prepare summary tables, and support operational reporting.

Data-Focused Work Experience Examples

Operations Assistant

• Analysed weekly operational data to track task completion, delays, and process issues.
• Prepared Excel reports using formulas, filters, and pivot tables for management review.
• Maintained accurate records and identified missing or inconsistent information.
• Supported process improvement by highlighting repeated delays and reporting gaps.

Sales Executive

• Tracked sales performance, customer follow-ups, and conversion activity using spreadsheets.
• Analysed monthly sales data to identify high-performing products and customer segments.
• Prepared performance summaries for managers, including revenue trends and pipeline status.
• Used customer data to prioritise follow-ups and improve sales activity planning.

Customer Service Advisor

• Managed customer information and maintained accurate case records for reporting.
• Identified repeated customer issues and shared trend summaries with the team.
• Used spreadsheet reports to track response times, complaint categories, and resolution status.
• Communicated insights clearly to support service improvement.

Finance Assistant

• Prepared financial reports using Excel, including expense summaries and payment tracking.
• Checked data accuracy, reconciled records, and identified missing or duplicate entries.
• Used formulas and pivot tables to summarise monthly costs and payment status.
• Supported reporting accuracy by validating figures before submission.

6. Education and Certifications

Your education section should include your degree, college, or relevant qualifications.

If you have completed a data analytics course, mention the tools and projects, not just the certificate name.

Weak example

Completed Data Analytics Course.

Strong example

Completed practical Data Analytics training covering Excel, SQL, Power BI, data cleaning, dashboard creation, business analysis, and portfolio projects.

Example format

Data Analytics Programme - Brit Institute

Covered Excel, SQL, Power BI, data cleaning, dashboard design, business reporting, Python basics, and portfolio project development.

Projects completed: Sales dashboard, HR analytics dashboard, SQL business case study, customer analysis report.

7. Data Analyst CV Keywords

Many employers and applicant tracking systems scan CVs for relevant keywords. Your CV should naturally include skills from the job description.

Useful Data Analyst CV keywords include:

• Data analysis
• Data cleaning
• SQL
• Excel
• Power BI
• Tableau
• Dashboard
• Reporting
• KPI
• Business intelligence
• Data visualisation
• Pivot tables
• Power Query
• DAX
• Python
• Pandas
• Data modelling
• Trend analysis
• Customer analysis
• Sales analysis
• Stakeholder communication
• Problem-solving
• Business reporting
• Forecasting
• Data quality
• ETL basics
• Database
• Insights

Do not stuff keywords randomly. Use them naturally in skills, projects, and experience.

8. How to Write Strong CV Bullet Points

A strong bullet point should show action, tool, task, and result.

Use this structure:

Action verb + data task + tool or method + business purpose

Example formula

Analysed sales data using SQL and Power BI to identify monthly revenue trends and top-performing product categories.

Strong action verbs

• Analysed
• Cleaned
• Created
• Built
• Designed
• Prepared
• Developed
• Automated
• Reported
• Visualised
• Identified
• Improved
• Calculated
• Presented
• Summarised
• Validated
• Tracked

Strong CV bullet examples

• Cleaned and prepared raw sales data by removing duplicates, correcting date formats, and validating missing values.
• Built a Power BI dashboard showing revenue, profit, monthly trends, regional performance, and product-level insights.
• Used SQL joins and aggregations to combine customer, order, and product tables for analysis.
• Created Excel pivot tables and charts to summarise performance by department, region, and month.
• Analysed customer behaviour to identify inactive users, repeat customers, and high-value segments.
• Prepared KPI reports to support management review and business decision-making.
• Presented dashboard insights clearly, explaining trends, risks, and recommended next steps.

9. CV Example for a Beginner Data Analyst

Professional Profile

Aspiring Data Analyst with practical skills in Excel, SQL, Power BI, data cleaning, and dashboard creation. Built portfolio projects involving sales analysis, HR analytics, customer behaviour, and SQL reporting. Able to clean messy data, calculate KPIs, build dashboards, and explain insights clearly for business decision-making.

Technical Skills

• Excel: Pivot tables, formulas, charts, XLOOKUP, conditional formatting
• SQL: SELECT, WHERE, JOIN, GROUP BY, HAVING, CASE WHEN
• Power BI: Power Query, DAX basics, KPI cards, slicers, dashboard design
• Data Analysis: Data cleaning, trend analysis, KPI reporting, business insights
• Python: Pandas basics, CSV cleaning, data preparation

Portfolio Projects

Sales Performance Dashboard

Built a Power BI dashboard to analyse monthly sales, regional performance, and top products. Cleaned raw sales data, calculated KPIs, and identified the highest revenue-driving categories.

HR Analytics Dashboard

Created an HR dashboard to analyse attrition, headcount, salary distribution, and department performance. Used Excel and Power BI to clean data and visualise workforce trends.

SQL Customer Analysis

Used SQL to join customer and order tables, calculate repeat purchases, identify inactive customers, and summarise revenue by customer segment.

10. CV Example for a Career Switcher

Professional Profile

Career switcher with professional experience in business operations and practical training in data analytics. Skilled in Excel, SQL, Power BI, reporting, and data cleaning. Able to combine domain knowledge with analytical tools to identify trends, improve reporting, and support business decision-making. Built portfolio projects in sales, operations, and customer analytics.

Transferable Experience

• Operations reporting
• Customer data management
• Performance tracking
• Spreadsheet analysis
• Business communication
• Process improvement
• Problem-solving
• Team collaboration

Portfolio Focus

• Operations performance dashboard
• Sales KPI report
• Customer segmentation analysis
• SQL business case study
• Power BI management dashboard

Strong positioning statement

My previous experience gives me practical business understanding, while my data analytics training gives me the technical skills to analyse data, build dashboards, and communicate insights clearly.

11. CV Example for a Graduate

Professional Profile

Recent graduate with strong interest in data analytics and business intelligence. Skilled in Excel, SQL, Power BI, and basic Python. Completed practical analytics projects involving data cleaning, dashboard creation, KPI reporting, and business insight generation. Seeking an entry-level Data Analyst role to apply analytical thinking and technical skills in a professional environment.

Project Strengths

• Academic research and analysis
• Excel reporting
• SQL practice projects
• Power BI dashboards
• Presentation skills
• Problem-solving
• Attention to detail

CV positioning

As a graduate, your CV should focus on projects, technical skills, coursework, internships, volunteering, and any experience involving analysis, reporting, research, or problem-solving.

12. How to Tailor Your CV for Each Job

A common mistake is sending the same CV to every employer.

Before applying, read the job description carefully and look for:

• Tools mentioned
• Main responsibilities
• Industry
• Required experience
• Soft skills
• Reporting requirements
• Dashboard or SQL expectations

Then adjust your CV to match the role.

Example

If the job description mentions SQL and Power BI, make sure your CV clearly shows SQL and Power BI projects.

If the role is in finance, highlight finance KPI, reporting, accuracy, and Excel experience.

If the role is in marketing, highlight campaign analysis, customer segmentation, conversion rate, and performance reporting.

If the role is in operations, highlight process improvement, tracking, dashboards, and performance metrics.

13. How to Make Your CV ATS-Friendly

Many companies use applicant tracking systems to manage applications. An ATS-friendly CV is simple, clear, and easy to read.

Use

• Clear headings
• Simple formatting
• Standard fonts
• Bullet points
• Relevant keywords
• Reverse chronological order
• Plain section titles
• PDF format, unless the employer asks for Word

Avoid

• Images or icons for important information
• Complex graphics
• Text boxes that may not scan properly
• Unusual fonts
• Two-column layouts that confuse parsing
• Keyword stuffing
• Long paragraphs
• Hidden text

Your CV should look professional but remain easy to read.

14. Common Data Analyst CV Mistakes

Mistake 1: Writing a generic profile

Avoid vague statements. Make your profile specific to data analytics.

Mistake 2: Listing tools without evidence

Do not only say "SQL, Power BI, Python". Show projects where you used them.

Mistake 3: Hiding projects at the bottom

If you are a beginner, your projects may be your strongest evidence. Place them near the top.

Mistake 4: Making the CV too long

For most junior roles, one to two pages is enough.

Mistake 5: Not showing business impact

A Data Analyst CV should show how your work supports decisions, reporting, or performance improvement.

Mistake 6: Using too much technical language

Your CV should be understandable to both recruiters and hiring managers.

Mistake 7: Not tailoring the CV

A CV for a Power BI Analyst role should look different from a CV for a SQL Analyst role.

Mistake 8: Claiming advanced skills without confidence

Only include tools and skills you can discuss in an interview.

15. What to Include If You Have No Experience

If you have no professional Data Analyst experience, focus on practical proof.

Include:

• Portfolio projects
• Data analytics course work
• Excel practice projects
• SQL case studies
• Power BI dashboards
• Python data cleaning examples
• Academic analysis projects
• Volunteering or freelance reporting
• Transferable work experience

Beginner-friendly CV headline

Aspiring Data Analyst | Excel, SQL, Power BI | Portfolio Projects in Sales, HR and Customer Analytics

Beginner-friendly project line

Built three portfolio projects using Excel, SQL, and Power BI to demonstrate data cleaning, KPI analysis, dashboard creation, and business insight presentation.

16. Data Analyst CV Layout Recommendation

Page 1

• Name and contact details
• Professional profile
• Technical skills
• Portfolio projects
• Most relevant experience

Page 2

• Additional work experience
• Education
• Certifications
• Additional skills
• Achievements

For beginners, the first page should quickly show that you have practical data skills.

17. How to Add LinkedIn and Portfolio Links

Your CV becomes stronger when it connects to proof.

Add links to:

• LinkedIn profile
• GitHub
• Power BI portfolio
• Personal website
• Project screenshots
• PDF case studies
• Tableau Public profile

Your portfolio should be clean and easy to understand.

Each project page should show:

• Project title
• Business problem
• Tools used
• Dataset summary
• Dashboard screenshot
• Key insights
• What you learned

18. Strong CV Lines for Data Analyst Applications

You can adapt these lines depending on your experience.

• Cleaned and transformed raw datasets to prepare accurate reports and dashboards.
• Created Power BI dashboards to track KPIs, trends, and business performance.
• Used SQL queries to extract, join, and summarise data from multiple tables.
• Analysed sales trends to identify top-performing products and regions.
• Prepared Excel reports using pivot tables, formulas, and visual summaries.
• Identified missing values, duplicates, and inconsistent formats during data cleaning.
• Presented insights in a clear, business-focused format for decision-making.
• Built portfolio projects covering sales, HR, customer, and operations analytics.
• Used data storytelling techniques to explain trends, risks, and recommendations.
• Documented analysis steps to make project work clear and repeatable.

19. Data Analyst CV Checklist

Before sending your CV, check:

• Is the CV tailored to the job description?
• Does the profile clearly mention data analytics?
• Are Excel, SQL, and Power BI visible near the top?
• Are projects explained with business context?
• Are bullet points specific and practical?
• Is the CV one to two pages?
• Are contact details correct?
• Is the LinkedIn link updated?
• Are there spelling or formatting mistakes?
• Can you explain every skill listed?
• Does the CV show evidence, not only claims?

20. How Brit Institute Helps Learners Build a Strong Data Analyst CV

Brit Institute helps learners develop the practical skills and project evidence needed for a stronger Data Analyst CV.

Our training focuses on:

• Excel reporting
• SQL queries
• Power BI dashboards
• Data cleaning
• Business analysis
• Python basics
• AI tools
• Portfolio projects
• Project explanation
• CV improvement
• LinkedIn guidance
• Interview preparation

The aim is not only to complete a course. The aim is to build a profile that shows practical ability, confidence, and career readiness.

Final Advice

A strong Data Analyst CV does not need to be complicated. It needs to be clear, relevant, and evidence-based.

Show the tools you know.

Show the projects you built.

Show the business problems you solved.

Show that you can explain data clearly.

If you are starting from zero, your CV should not apologise for lack of experience. It should demonstrate preparation, practical learning, and proof of skill.

A good CV helps employers see that you are not just interested in data analytics - you are actively building the skills needed to do the job.

Start Your Data Analytics Career

Ready to strengthen your Data Analyst CV with practical projects and career support?

• Explore Data Analytics Programme
• Book a Free Career Guidance Call
• Download Curriculum
• Speak to a Career Advisor