Data analyst CV example and writing guide

The question, the method, the decision

Most data analyst CVs read like a list of software. The hiring manager already assumes you can write a SELECT statement; the advert asked for it. What they cannot guess is whether your work changed anything. Bullets that get interviews tend to read as a small story: someone needed to know something, you found out, and a decision followed.

That third part is the one people leave off, often because the decision was made by someone else. Include it anyway. "The repairs team moved two engineers to the Slough patch after my report" is your result even though you did not sign the rota. It proves your output reached a person who acted on it, which is the whole job.

If you have a figure, use it and be ready to explain how you got it. If you do not, describe the change in words. "Replaced a monthly spreadsheet that took two people most of a day to build" is honest and specific without a number you cannot defend.

How to present your tools without a logo wall

Recruiters and applicant tracking systems search for exact tool names, so you do need a skills section. Keep it short and group it by what you do rather than listing every package you have opened:

• Querying and data preparation: SQL (say which dialect if you know it, such as T-SQL or PostgreSQL), Power Query, Python with pandas.
• Analysis: Excel (pivot tables, lookups, what-if), Python or R for statistics, A/B test reads, forecasting if you have done it for real.
• Reporting: Power BI or Tableau, DAX if you write measures yourself, report automation.
• Ways of working: version control with Git, data dictionaries, documenting a model so someone else can maintain it.

Leave out star ratings and percentage bars. They cannot be checked, applicant tracking systems often cannot read them, and they invite an interviewer to test the one you rated highest. If you used Python twice on a course, say "Python (pandas, beginner)" or leave it off. A data team will find out in the technical test anyway, and honesty about level reads as maturity.

A portfolio that helps rather than hurts

A GitHub link or a public Power BI or Tableau report is worth adding when you are junior, changing career, or your paid work is all behind a company login. One or two finished pieces beat ten half-done notebooks. Pick a public dataset about something the employer cares about, write a short README that states the question and what you found, and make sure the code runs.

Never publish work data, even with names removed. A reviewer who spots customer records in a public repository will not hire you, however good the analysis is. If you want to show work projects, describe them on the CV and bring a screenshot with the numbers blurred to interview.

Moving into data from another job

Plenty of analysts start somewhere else: customer service, finance admin, retail management, operations. Your earlier job is an asset, because you understand where the data comes from and what the people using the reports need. The CV has to make that link for the reader.

Under the old job, keep one or two bullets that show you already worked with data, such as building a tracking spreadsheet, spotting a pattern in complaints or producing a team report. Then list the learning that got you across: a course, a certificate, an internal secondment. The National Careers Service lists apprenticeships at several levels, including a Data Technician Level 3 and a Data Analyst Level 4, and some employers offer them to existing staff, which is one of the more reliable ways across.

In your summary, say what you are now, not what you are leaving. "Data analyst with three years in customer insight, previously a customer service adviser" is stronger than "Customer service professional looking to move into data".

Junior, mid-level and senior: what changes on the page

Titles vary between employers, so readers judge your level from the scope of your bullets. A junior analyst CV shows accurate, reliable work: reports delivered on time, data cleaned, questions from one team answered. A mid-level CV shows you choosing the approach, owning a data source end to end and explaining results to managers who push back. A senior or lead CV shows you setting standards: defining metrics other teams adopt, reviewing others' work, deciding what not to build.

Order bullets so the most senior-looking work comes first under each role. If you mentored an apprentice or wrote the team's SQL style guide, it belongs near the top, not at the end.

How data jobs are screened

Expect two filters before an interview. The first is keyword matching, by software or by a recruiter with the job advert open. Use the advert's own words: if it says Power BI, write Power BI rather than "BI tools". Keep the layout to one column with standard headings so the text parses cleanly.

The second is a technical test. Data teams often send a SQL exercise, a spreadsheet task or a take-home dataset with a question and a deadline. Your CV sets up that test. If it claims window functions and you then struggle with a GROUP BY, the mismatch counts against you more than the gap would have. Write only what you could do with the clock running.

Tailoring to the sector you are applying to

Analysis skills move between sectors, but vocabulary does not. A housing association will talk about arrears, voids, repairs, complaints and tenant satisfaction. A retailer talks about basket size and stock cover. An NHS trust talks about waiting lists and activity data. Read the job description, note the nouns, and check which of your projects touched something similar. A customer insight analyst at a utility has worked on complaints, contact volumes and vulnerable customer data, all of which map directly to social housing.

Put that link in the summary and in the first bullet of your latest job. You do not need to have worked in the sector before; you need the reader to see that you already understand problems like theirs.

Mistakes that cost data analyst interviews

• Percentages with no baseline, such as "improved efficiency by 30%". Say what was measured, or describe the change in words.
• Bullets that describe the dashboard rather than what anyone did with it.
• Claiming machine learning because you once ran a regression. Say regression.
• A two-column template with skill bars that an applicant tracking system reads out of order.
• Leaving out the domain: the reader wants to know you analysed complaints data, not just "data".
• Typos in tool names. PowerBI, Sequel and Pyhton all get noticed.

Data analyst pay

The National Careers Service gives an average of £28,000 a year for a starter data analyst-statistician and £65,000 for an experienced one. Where you sit depends on sector, location and how technical the role is.