Data analyst roles are one of the most competitive tech positions in 2026, with employers receiving hundreds of applications for each role. Your CV is the gatekeeper. Get it right and you land interviews. Get it wrong and the ATS never passes it to a human.
The good news is that data analyst CVs follow a fairly predictable structure once you know what recruiters and ATS systems look for. This guide walks through each section with specific examples, a full CV template, and the most common mistakes to avoid.
What Makes a Strong Data Analyst CV?
Data analyst CVs need to do 4 things well:
- Include the right technical keywords (SQL, Python, Tableau, Power BI, Excel)
- Show measurable business impact (not just technical skills)
- Demonstrate analytical thinking through achievements, not adjectives
- Pass ATS screening with clean formatting and matched keywords
According to recent data, SQL appears in 57% of data analyst job postings, Python in 52%, Tableau in 40%, and Power BI in 38%. If you have these skills, name them specifically.
Data Analyst CV Structure
A strong data analyst CV follows this structure:
- Contact details (with LinkedIn and GitHub links)
- CV summary (3-4 sentences)
- Technical skills (grouped by category)
- Work experience (with quantified achievements)
- Education (degree and relevant coursework)
- Certifications and projects (optional but valuable)
Full Data Analyst CV Example
Liam Carter
London, UK | 07700 900123 | liam.carter@email.com
LinkedIn: linkedin.com/in/liamcarter | GitHub: github.com/liamcarter
Engineering CV Examples
---
Word Count Target: 2,000-2,500
Data Analyst | 5 Years of Experience in B2B SaaS Analytics
---
Summary
Data analyst with 5 years of experience transforming raw datasets into actionable business intelligence in B2B SaaS. Built automated Tableau dashboards that reduced manual reporting by 60% and identified pricing anomalies saving £200K annually. Proficient in SQL (PostgreSQL, BigQuery), Python (pandas, NumPy), and Tableau. Specialises in revenue analytics and marketing attribution.
---
Technical Skills
- Languages: SQL (PostgreSQL, BigQuery, Snowflake), Python (pandas, NumPy, matplotlib), R, DAX
- Visualisation: Tableau, Power BI, Looker Studio, matplotlib
- Tools: Git, dbt, Airflow, Alteryx, Excel (advanced)
- Methods: A/B testing, cohort analysis, marketing attribution, regression analysis
---
Work Experience
Senior Data Analyst | TechFlow | Jan 2023 - Present
- Built 12 self-serve Tableau dashboards for sales, marketing, and customer success teams, reducing manual reporting requests by 60% (from 45 per month to 18)
- Led a pricing analysis that identified 3 underperforming pricing tiers, resulting in a restructuring that added £200K in annual recurring revenue
- Designed and ran 15 A/B tests on the product onboarding flow, increasing activation rates from 42% to 58% over 9 months
- Mentored 2 junior analysts through their first 6 months, both of whom were promoted within 18 months
Data Analyst | Fintech Co | Aug 2020 - Dec 2022
- Analysed customer behaviour data using Python and SQL, producing weekly churn reports that informed retention campaigns. Contributed to a 15% reduction in monthly churn over 12 months
- Built the company's first marketing attribution model, integrating data from Google Ads, HubSpot, and Stripe, which shifted 25% of paid budget to higher-performing channels
- Automated 8 recurring reports using Python and SQL, saving the team approximately 12 hours per week
Junior Data Analyst | Consulting Co | Sep 2019 - Jul 2020
- Delivered 6 client engagements across retail and financial services, producing bespoke dashboards and ad-hoc analyses
- Built a sales forecasting model for a retail client using Python (scikit-learn) that reduced forecast error by 22%
---
Education
BSc Mathematics and Statistics | University of Manchester | 2016-2019
- First-class honours (79% average)
- Dissertation: "Machine Learning Approaches to Customer Churn Prediction" (scored top 5% in cohort)
- Relevant modules: Statistical Inference, Multivariate Analysis, Programming in R, Applied Statistics
---
Certifications
- Google Data Analytics Professional Certificate (2023)
- Tableau Desktop Specialist (2022)
- AWS Cloud Practitioner (2021)
---
Projects
- Churn Prediction Model (Open Source): Built a Python-based churn prediction model for a public B2B dataset; model achieved 82% accuracy. Code available on GitHub.
- Data Visualisation Portfolio (Tableau Public): 12 interactive dashboards on topics including UK housing prices, London transport, and public spending.
Section-by-Section Writing Guide
Contact Details
Keep the top of your CV simple and scannable. Include:
- Full name
- Location (city only; no full address)
- Phone number
- Professional email
- LinkedIn URL (essential)
- GitHub URL (highly recommended)
- Portfolio URL (if you have one)
No photo, no date of birth, no marital status.
CV Summary
Your CV summary is the first thing a recruiter reads. Keep it to 3-4 sentences covering:
- Years of experience and domain (e.g., "5 years in B2B SaaS analytics")
- 1 or 2 quantified achievements (specific metrics)
- Core technical skills (SQL, Python, Tableau)
- Specialisation (revenue analytics, marketing attribution, etc.)
Technical Skills Section
Your skills section for data analyst roles should be grouped by category. Avoid giant comma-separated lists.
Sample categories:
- Languages: SQL (specify flavour), Python, R
- Visualisation: Tableau, Power BI, Looker Studio
- Tools: Git, dbt, Airflow, Excel
- Methods: A/B testing, regression, cohort analysis
Include specific libraries where relevant (pandas, NumPy, scikit-learn, matplotlib). Name the SQL dialect you know best.
Work Experience
This is the most important section. Follow these rules:
Rule 1: Start every bullet with a strong verb
Use: Built, Designed, Led, Analysed, Automated, Launched, Scaled, Reduced, Improved.
Avoid: Responsible for, Helped with, Worked on.
Rule 2: Quantify every bullet you can
Numbers are what separate shortlisted CVs from discarded ones. Include:
- Revenue generated or saved
- Percentage improvements
- Team size or scale of data
- Time saved
- Number of reports, dashboards, or projects
Rule 3: Lead with impact, follow with method
Weak: "Used SQL and Python to analyse customer data"
Strong: "Reduced monthly churn by 15% through SQL-based cohort analysis that informed retention campaigns"
Rule 4: Mirror language from the job description
If the posting says "dashboard development," use that phrase. If it says "ETL pipelines," use that phrase. Exact matches help with ATS screening.
Education
Include your degree, institution, dates, and any standout details:
- Grade (if strong: first-class, 2:1, or top 20%)
- Relevant dissertation topic (with score if impressive)
- Relevant modules (list 3-5 that map to the role)
Freshers can expand this section. Experienced analysts should keep it concise.
Certifications
List relevant certifications with year:
- Google Data Analytics Professional Certificate
- Microsoft Certified: Data Analyst Associate
- Tableau Desktop Specialist
- AWS Data Analytics Specialty
- Any SQL certifications
Projects
For entry-level and early-career analysts, a projects section is gold. Include:
- Personal data analysis projects
- Kaggle competitions with rankings
- Open-source contributions
- Tableau Public dashboards
Always include links where possible.
20 Data Analyst CV Bullet Point Examples
Use these as inspiration, adapted to your own experience.
Revenue and business impact
- Built a pricing analysis model that identified £200K in annual revenue opportunities across 3 underperforming tiers
- Reduced customer acquisition cost by 22% through marketing attribution analysis in Python
- Delivered sales forecasting that reduced forecast error from 18% to 8% over 6 months
Dashboard and reporting
- Designed 12 self-serve Tableau dashboards used daily by 40+ stakeholders across sales, marketing, and customer success
- Automated 8 recurring reports using Python and SQL, saving approximately 12 hours per week
- Replaced a manual monthly reporting process with an end-to-end dbt + Looker pipeline, cutting delivery time from 5 days to 1
Experimentation and testing
- Designed and analysed 15 A/B tests on the product onboarding flow, increasing activation rates from 42% to 58%
- Ran pricing experiments across 3 customer segments, leading to a 12% ARPU uplift in the target segment
Data infrastructure
- Built the company's first dbt-based analytics layer, replacing 40+ ad-hoc SQL queries with a governed data model
- Migrated legacy reporting from Excel to a Snowflake + Tableau stack, reducing refresh latency from 24 hours to 15 minutes
Machine learning and advanced analysis
- Built a churn prediction model in Python (scikit-learn) with 82% accuracy, now used to drive monthly retention campaigns
- Delivered a customer segmentation model using k-means clustering that identified 4 high-value cohorts for targeted outreach
Team and leadership
- Mentored 3 junior analysts, all of whom progressed to senior analyst roles within 18 months
- Led a working group of 5 cross-functional stakeholders to define data governance standards adopted company-wide
Specific technical depth
- Built SQL queries against a 5TB PostgreSQL warehouse with performance tuning that reduced average query runtime by 40%
- Developed Python data pipelines using pandas and SQLAlchemy, processing 20M+ transactions per month
Process and efficiency
- Reduced monthly close reporting time by 60% through automation in Python and SQL
- Introduced weekly data reviews that surfaced 6 significant data quality issues in the first quarter
Client or stakeholder-facing
- Presented quarterly analytical findings to the CFO and CEO; 3 recommendations adopted directly into the pricing strategy
- Delivered 6 client-facing dashboards across retail and fintech engagements, all rolled out within agreed timelines
ATS Tips for Data Analyst CVs
1. Use exact keywords from the job description
If the posting says "Power BI," use "Power BI" on your CV, not "MS BI." If it says "Python," use "Python," not just "scripting."
2. Avoid tables, columns, and graphics
Multi-column layouts confuse many ATS parsers. Stick to a single-column layout.
3. Use standard section headings
"Experience," "Education," "Skills," and "Certifications" parse reliably. Creative headings like "Where I Have Worked" can break parsing.
4. Submit as PDF
Unless the employer specifically asks for Word, PDF is safer for preserving formatting.
5. Include both abbreviations and full terms
"Structured Query Language (SQL)" and "Business Intelligence (BI)" help with ATS searches that look for either form.
6. Spell out numbers
Write "5 years of experience" in the summary rather than relying on symbols or unusual characters that ATS can mis-parse.
For a deeper look at ATS systems, see our full ATS guide.
Entry-Level vs Senior Data Analyst CV Differences
Entry-level (0-2 years)
- Lead with education and academic projects
- Include relevant coursework, dissertation topic, and grades
- Emphasise personal projects, Kaggle, open-source contributions
- Include internships and student consulting work
- Use a resume objective rather than a summary
See our student CV guide for more on building a CV with limited experience.
Mid-level (2-5 years)
- Lead with work experience
- Use a CV summary highlighting 2-3 achievements
- De-emphasise education (move below experience)
- Include specific tech stack and certifications
- Show progression from junior to mid-level responsibilities
Senior (5+ years)
- Emphasise business impact over technical depth
- Lead with strategic contributions (roadmap influence, team leadership)
- Include mentoring, hiring, and team-building experience
- List certifications, talks, and thought leadership if relevant
- Use a strong resume headline at the top
Common Data Analyst CV Mistakes
1. Skill lists without depth
"Python" alone tells the recruiter nothing. "Python (pandas, NumPy, scikit-learn)" shows depth.
2. Vague bullets without numbers
"Improved reporting" is meaningless. "Reduced monthly reporting time by 60%" is interview bait.
3. Listing tools you barely know
If you list R and the interviewer asks basic questions, you will be caught out. Only list what you can discuss confidently.
4. Hiding key skills in long paragraphs
ATS parsers and human scanners both prefer clean skills sections. Do not bury technical skills inside a narrative summary.
5. Missing GitHub or portfolio
For data roles, a GitHub or Tableau Public portfolio is expected. Not linking one signals inexperience.
6. Irrelevant early work
If you worked in retail for 3 years before becoming an analyst, that is useful context, but do not spend 6 bullet points on it.
7. Poor formatting
Fancy fonts, colour accents, and infographics break ATS parsing. See our best font for CV guide.
Frequently Asked Questions
Should I include non-data work experience on my data analyst CV?
Yes, but briefly. If you spent 3 years in customer support before moving into analytics, one or two short bullets is enough. Do not delete it, but do not emphasise it either.
Is a cover letter necessary for data analyst roles?
Usually yes, unless the application explicitly says not to include one. See our motivation letter guide or letter of interest guide.
How do I handle a career change into data analytics?
Lead with certifications (Google Data Analytics, etc.), a strong resume objective that bridges your past experience, and personal or bootcamp projects. Transferable skills like quantitative reasoning, stakeholder management, and process thinking also help.
Should I mention Excel if I have SQL and Python?
Yes, but briefly. Excel is still expected at most data analyst roles (especially in non-tech companies). Note "Excel (advanced: PivotTables, Power Query, PivotCharts)" rather than just "Excel."
What if the job posting asks for a skill I do not have (e.g., R)?
Do not list it if you do not have it. Instead, emphasise the adjacent skills (Python, SQL) and address the gap in your cover letter if the skill is important.
Key Takeaways
- Lead with technical skills (SQL, Python, Tableau) that match the job posting
- Include specific SQL dialects and Python libraries, not just "SQL" or "Python"
- Quantify every work experience bullet where possible (revenue, percentages, time saved)
- Organise skills by category rather than a comma-separated list
- Include LinkedIn and GitHub links at the top
- Add a projects section or portfolio link for early-career candidates
- Optimise for ATS screening with clean formatting and exact keyword matches