Data Analyst CV Examples & Writing Guide

This sample is for an analyst with about four years of experience, the level most people searching for this page are at or aiming for. The name, employers and university are made up. If you are a graduate or looking for your first analyst job, the entry-level section below shows how to fill the same layout with projects and placements.

Analytics managers and the recruiters who screen for them read hundreds of data analyst CVs, most of them listing the same tools. Four things separate the interview pile from the rest.

• SQL you have actually written. Not "SQL" alone but the dialect (Snowflake, BigQuery, PostgreSQL, SQL Server, MySQL) and a bullet that shows the scale (tables, rows, models) and complexity (window functions, CTEs, performance tuning). Most interview processes include a live SQL exercise or technical test, so managers check the CV promises it.
• A business result, not a technical one. "Reduced query time" is nice. "Found the drop-off that led to a 0.6-point conversion lift" gets the interview. Every experienced analyst needs at least one bullet where a decision changed because of their work.
• One visualisation tool in depth. Tableau or Power BI (Looker, Sigma and Qlik in some companies) with the number of dashboards and the number of people who use them. Listing five BI tools reads as none.
• For entry level: projects with real data and a link. A GitHub repository or Tableau Public profile with two or three clean projects (question, data source, method, finding) does more than any certificate.

“I skip to the bullets and look for a number that a business person would care about. If every line ends in a dashboard or a report and nothing ever changed because of it, I assume they were a report factory.”

Three to four lines under the header. Years and sector first, then the tools you use daily, then your best result with a number, then certifications if they matter. Skip the career objective; even a new graduate has a project to describe.

Use this pattern: data (source, size), method (tool, technique), result (what changed, for whom, by how much). Below are common weak bullets rewritten. Change the numbers to your own; interviewers ask about every one.

Group skills by category and keep the list to 12 to 16 items. Name the dialect or version where it matters. Recruiters search applicant tracking systems for exact tool names; "data visualisation" alone does not match "Tableau".

• Querying: SQL (name the databases: PostgreSQL, MySQL, SQL Server, Snowflake, BigQuery, Redshift, Databricks), window functions, CTEs, query optimisation
• Visualisation and BI: Tableau, Power BI (DAX), Looker (LookML), Sigma, Qlik Sense, Google Looker Studio
• Programming: Python (pandas, NumPy, matplotlib, seaborn, scikit-learn), R (tidyverse, ggplot2), Jupyter
• Spreadsheets: Excel (pivot tables, Power Query, XLOOKUP, Solver), Google Sheets
• Data modelling and pipelines: dbt, Airflow basics, data warehousing concepts, star schemas
• Statistics: A/B testing, hypothesis tests, regression, confidence intervals, sampling, forecasting
• Product and web analytics: Google Analytics 4, Amplitude, Mixpanel, Adobe Analytics
• Tools and governance: Git and GitHub, Jira, Confluence, Snowflake or BigQuery consoles, Salesforce reports if relevant; working with personal data under UK GDPR (access controls, anonymisation, retention)
• Sector: e-commerce funnels, insurance or healthcare claims, supply chain KPIs, financial reporting, public sector data, whichever you have worked in

Do not list soft skills as words. "Communication" means nothing; "monthly readout to the Operations Director, 4 of 6 recommendations adopted" shows it. The same goes for "attention to detail": show the error rate you cut.

Projects are the main content of a graduate's CV and a useful extra for anyone with under two years of experience. A project entry should read like a job bullet: the question, the data (source and size), the method and tools, and the finding. Link the code or dashboard. Two or three strong projects beat eight course exercises.

1. 1Pick a real dataset, not the Titanic or Iris sets every recruiter has seen: UK open data (data.gov.uk, ONS, local council and transport data), Kaggle competitions with messy data, a public API, your own scraped or exported data.
2. 2Start from a business-style question ("which neighbourhoods have rising fly-tipping reports and why?") rather than a technique.
3. 3Do the work in SQL and one visualisation tool, plus Python if you use it. Clean the data and say how much cleaning was needed.
4. 4Write a README with the question, method, findings and two charts. Recruiters click the link and spend 30 seconds; the README is what they see.
5. 5On the CV: title, tools, one or two lines with the size of the data and the finding, and the link. Add a Tableau Public link if the project is a dashboard.

"No experience" usually means no job with "analyst" in the title. Almost everyone has analysed data somewhere: a dissertation or module project, a part-time job's spreadsheets, a society's membership list, a placement. Here is the order that works when the experience section is thin.

• Header with a GitHub or Tableau Public link that actually has projects in it. An empty profile is worse than no link.
• Personal statement of three lines: degree, apprenticeship or certificate, tools, and your best project with a number.
• Education near the top: degree with classification (or predicted classification), graduation date, relevant modules (Statistics, Database Systems, Econometrics, Data Visualisation), and A-level grades if you graduated recently. For a bootcamp, certificate or Level 4 Data Analyst apprenticeship, list it here with the length.
• Projects section with 2 to 3 entries written as shown above. This is your experience section for now.
• Any job, rewritten around data: a retail job where you tracked stock in Excel, a research assistant role where you cleaned survey data in R, a student job where you built the sign-up report. Give the size of the data and what someone did with your numbers.
• Certifications: Google Data Analytics Professional Certificate, Microsoft PL-300, Tableau Desktop Specialist, IBM Data Analyst Professional Certificate. Useful as keyword matches, but a manager will weigh the projects more.
• Skills section with exact tool names, honest about level. Say "Python (pandas, matplotlib)" not "Python expert" after one course.

• Length: one page works up to a few years of experience; two pages are normal in the UK for analysts with longer project lists, senior analysts and people managers. Recruiters read the first half page and decide.
• Order for experienced analysts: header, personal statement, experience, skills, certifications, education. Entry level: header, personal statement, education, projects, experience, skills, certifications.
• Layout: single column, clear headings, no graphics or skill bars. A clean template such as cvplex modern-blue passes applicant tracking systems (ATS) and looks current. No photo and no date of birth on a UK CV.
• Links: LinkedIn, GitHub and Tableau Public in the header as plain text URLs. Make sure the GitHub has READMEs and the Tableau Public dashboards load.
• Keywords to mirror from the advert when they are true for you: SQL, Tableau or Power BI, Python, Excel, dashboards, KPIs, A/B testing, data cleaning, data modelling, stakeholder, reporting, forecasting, plus the exact database (Snowflake, BigQuery, Redshift). The ATS matches exact strings, so write "Power BI" not "PowerBI".
• CV or resume? In the UK it is a CV, usually one to two pages. US employers call the same document a resume and prefer it shorter. Academic CVs (publications, every module) are for research roles, not analyst jobs.