Data Analyst CV Example
Updated 22 June 2026
A strong data analyst CV proves you can turn raw data into business value. This guide shows you how to write a CV that passes ATS screening, demonstrates technical depth in SQL and Python, and translates your analysis into plain-language impact that non-technical recruiters and hiring managers will understand.
Data Analyst CV examples
Graduate Data Analyst
entryLeads with core tool stack, uses portfolio projects to substitute for limited experience, and quantifies every bullet with a metric.
Data Analyst
midEvery bullet ties a technical task to a business outcome with a metric, demonstrates data storytelling for non-technical stakeholders, and shows range across descriptive, diagnostic and prescriptive analysis.
Senior Data Analyst
seniorDemonstrates leadership, mentoring and strategic influence, with every bullet showing scale (multi-million-record datasets, cross-functional projects) and measurable business impact.
How to write a data analyst CV
Format and length
Keep your data analyst CV to two pages maximum (one page if you are a graduate or have under three years of experience). Use reverse-chronological order: most recent role first. A clean, single-column layout works best for ATS parsing.
Section order
- Contact details and personal statement at the top (name, location, phone, email, LinkedIn).
- Skills immediately after your personal statement so recruiters and ATS see your tool stack up front.
- Experience (or Projects if you are entry-level) in reverse-chronological order.
- Education after experience (unless you are a recent graduate with limited work history, in which case education can sit above experience).
- Certifications and additional info at the end if relevant.
Personal statement
Two to three sentences. State your experience level, core technical skills (SQL, Python, your BI tool), and the type of impact you deliver. Keep it recruiter-friendly rather than jargon-heavy. Entry-level template: "Diligent, detail-oriented graduate Data Analyst competent in SQL, Python and Excel, keen to apply analytical skills to data-driven decision-making."
Experience and projects
Every bullet must tie a technical task to a business outcome with a number. Show the type of analysis (descriptive, diagnostic, predictive, prescriptive) and make data storytelling explicit. If you have little work experience, build this section around two to three portfolio projects with a business question, the tools you used, and the result.
Skills
List SQL first (the most-screened keyword), then Python or R, then your BI tool (Tableau, Power BI, Looker). Mirror the exact tool names from the job advert. Include data cleaning and A/B testing as explicit skills if you have done them.
Education and certifications
List degree, institution, dates and honours. Add relevant certifications (Google Data Analytics, Microsoft Power BI, Tableau Desktop) under a separate Achievements or Certifications heading.
| Section | What to include | What to leave out |
|---|---|---|
| Personal statement | Experience level, core tools (SQL, Python, BI), type of impact | Generic phrases ("hard-working", "passionate"), unexplained jargon |
| Skills | Exact tool names from the advert, SQL first, data cleaning, A/B testing | Soft skills (save for bullets), tools you have only read about |
| Experience | Business outcome + metric for every bullet, plain-language insight | Duties without impact ("responsible for reporting"), unexplained acronyms |
| Education | Degree, institution, honours, relevant modules or dissertation | Secondary school (unless no higher education), unrelated coursework |
Personal statement examples
Results-driven Data Analyst with four years of experience turning data into actionable business insights. Skilled in SQL, Python and Tableau, with a track record of building dashboards that reduce decision-making time and running A/B tests that lift revenue. Adept at translating complex findings into plain language for non-technical stakeholders.
Hard-working and passionate data professional looking for a challenging role to leverage my skills in a dynamic, fast-paced environment. Strong team player with excellent communication skills and a keen eye for detail. Eager to contribute to a forward-thinking organisation.
Writing your experience
The result-plus-metric pattern
Every bullet should follow this structure: action verb + technical task + business outcome + metric. The metric can be dataset size, percentage improvement, revenue gained, cost saved, or time eliminated. This proves impact rather than listing duties.
Weak (duty-focused): Responsible for creating weekly sales reports in Power BI.
Strong (impact-focused): Automated weekly sales reporting in Power BI, cutting report-prep time by 80% (12 hours/month saved) and enabling same-day delivery of performance insights.
Weak: Analysed customer data to identify trends.
Strong: Analysed 2M+ sales records in SQL and surfaced regional pricing trends that informed a new pricing strategy, lifting revenue by 15% in the South West region over six months.
Weak: Conducted A/B tests on the website.
Strong: Ran A/B tests on product-page design and checkout flow, increasing conversion rate by 12% and average order value by 7%, translating to £180K additional monthly revenue.
Show the type of analysis
Frame your work by analysis type to demonstrate range:
- Descriptive: what happened ("Built Tableau dashboards tracking KPIs...")
- Diagnostic: why it happened ("Diagnosed why churn rose 9% by joining CRM and transaction data...")
- Predictive: what will happen ("Built predictive models forecasting campaign ROI...")
- Prescriptive: what to do ("Recommended a targeted email campaign that recovered 22% of at-risk customers")
Make data storytelling explicit
Do not assume the reader knows you communicated your findings. State it: "Presented findings to non-technical stakeholders in plain language, translating statistical results into actionable recommendations." This separates you from analysts who only run queries.
Action verbs for data analysts
Analysed, automated, built, cleaned, conducted, designed, diagnosed, forecasted, identified, optimised, presented, queried, ran, reduced, surfaced, translated, validated, visualised.
| Before (weak) | After (strong) |
|---|---|
| Responsible for data analysis and reporting. | Analysed 500K+ customer records in SQL, surfacing churn trends that informed a retention campaign and reduced churn by 14%. |
| Created dashboards in Tableau. | Built interactive Tableau dashboards tracking conversion rate and cart abandonment, cutting executive decision-making time by 35%. |
| Cleaned data for analysis. | Cleaned and validated 15,000+ survey responses in Python, identifying and correcting 8% of records with missing data to ensure research integrity. |
Key skills & ATS keywords
Hard skills
Soft skills
ATS keywords
Education & certifications
Education
List your degree, institution, dates and classification (First, 2:1, etc.). If you studied a quantitative subject (mathematics, statistics, economics, computer science), mention relevant modules or your dissertation topic, especially if it involved data analysis or modelling.
Recent graduates with limited work experience should place education above experience and expand the section with a dissertation summary or relevant coursework that demonstrates analytical skills.
Example:
University of Bath Bachelor of Science, Economics, 2018–2021 2:1 Honours Modules in econometrics, statistical methods and data analysis using R and Stata.
Certifications
Data analyst certifications signal commitment and fill gaps in formal education. List them under an "Achievements" or "Certifications" heading with the issuing body.
High-value certifications for data analysts:
- Google Data Analytics Professional Certificate (Coursera / Google) – widely recognised entry-level credential covering the full analytics workflow.
- Microsoft Certified: Power BI Data Analyst Associate – proves proficiency in Power BI, a top ATS keyword.
- Tableau Desktop Specialist / Certified Professional – demonstrates hands-on Tableau skills.
- Microsoft Certified: Azure Data Scientist Associate – for analysts moving into predictive modelling and machine learning.
- SQL certifications (e.g. "SQL for Data Science" from UC Davis on Coursera) – reinforces your anchor technical skill.
Do not list certifications you have not completed. If you are currently studying for one, mention it in your personal statement or under "Additional Info" as "In progress: [certification name], expected [month/year]".
No degree?
Many data analyst roles accept relevant certifications and demonstrable skills in place of a degree. Build a strong portfolio (see Projects section below) and stack certifications (Google Data Analytics + a BI tool cert + SQL) to prove competence. Lead with your skills and portfolio in the CV, and address the lack of a degree briefly in your cover letter if the role explicitly requires one.
Common mistakes to avoid
Listing tools without showing how you used them or what you achieved.
Every tool mention should tie to a bullet with a metric. "Proficient in SQL" means nothing; "Analysed 2M+ records in SQL, surfacing trends that lifted revenue 15%" proves capability.
Writing duties instead of outcomes ("responsible for reporting", "tasked with data analysis").
Reframe every bullet as impact: "Automated weekly reporting in Power BI, cutting prep time by 80% (12 hrs/month saved)." Show what changed because of your work.
Burying SQL or your BI tool deep in the CV, or not mentioning them at all.
List SQL first in your skills section and show it in at least one experience or project bullet. Name your BI tool explicitly (Tableau, Power BI, Looker), not "data visualisation" in the abstract.
Ignoring data cleaning and quality assurance, or treating it as too basic to mention.
Data cleaning is foundational for analysts and signals attention to detail. Add it as a skill and back it with a bullet: "Cleaned 15,000+ survey responses in Python, correcting 8% of records with missing data."
Using the same CV for every application without tailoring the skills section.
Mirror the exact tool keywords from the job advert ("Power BI" vs "Tableau", "Python" vs "R"). ATS does literal matching, and tailoring materially improves pass-through.
Failing to show that you communicated insights to non-technical stakeholders.
Make data storytelling explicit: "Presented findings to non-technical stakeholders in plain language, translating statistical results into actionable recommendations." This separates you from query-only analysts.
Junior vs senior: what changes
| Aspect | Junior | Senior |
|---|---|---|
| Personal statement | Leads with core tool stack (SQL, Python, Excel) and eagerness to apply skills. Mentions portfolio projects if limited work experience. | Leads with years of experience, leadership (mentoring, cross-functional projects) and strategic impact (revenue growth, cost savings at scale). |
| Experience bullets | Smaller datasets (thousands to low hundreds of thousands of records), foundational tasks (cleaning, exploratory analysis, basic dashboards). Metrics focus on time saved or process improvements. | Multi-million-record datasets, complex analysis (diagnostic, predictive, prescriptive), A/B testing with double-digit lifts. Metrics focus on revenue, cost savings, and strategic influence. |
| Skills section | Core tools only (SQL, Python or Excel, one BI tool). May include coursework tools (R, Stata) if recently graduated. | Broader and deeper stack (SQL + Python + R, multiple BI tools, cloud platforms like BigQuery or Snowflake, advanced stats and ML libraries). Includes leadership and mentoring. |
| Projects vs experience | May substitute portfolio projects for limited work experience. Projects should show end-to-end analysis (business question, data prep, visualisation, plain-language result) with links to GitHub or Tableau Public. | Work experience only. Projects are replaced by leadership achievements (mentoring, process design, cross-functional influence). |
| Certifications | Entry-level certs (Google Data Analytics, SQL for Data Science) to fill experience gaps and prove foundational competence. | Advanced certs (Azure Data Scientist, Tableau Certified Professional) and evidence of continuous learning. Certifications are supplementary, not the main credential. |
| Data storytelling | States that findings were presented or shared. May describe a single stakeholder presentation. | Shows repeated stakeholder influence, executive-level communication, and translation of complex analysis into strategic decisions. Quantifies the impact of communication ("enabling real-time monitoring of 12 KPIs"). |