Data Analyst CV example

What recruiters expect

Before writing anything, it helps to know what the person reading is checking for. In this field that is usually a short, specific list:

• SQL described by what it does in your work, with the constructs named rather than a self-assessed level.
• One BI tool used in depth, shown through a report with a named audience and a regular cadence.
• Evidence of stakeholder work: who asked the question, what you told them, what they did next.
• Data quality and definitions work, which fills a large part of many analyst roles and rarely reaches the CV.
• Statistical care at the right level: knowing when a result is inconclusive and saying so.

Recommended CV structure

This is the running order the example uses. It is a starting point rather than a rule, but the order reflects what tends to be read first in this profession.

1. Profile — Three or four lines positioning you for the role.
2. Technical Skills — Grouped skills, for example "Languages" and "Tooling".
3. Professional Experience — Paid roles, in reverse chronological order.
4. Analytical Projects — Work you built, with outcomes and the stack used.
5. Education — Degrees, diplomas and school-leaving qualifications.
6. Certifications — Completed certifications with the issuing body.
7. Languages — Spoken languages with CEFR levels.

Sections worth adding

• Training — Useful when moving into analytics from another field and the courses are recent.
• Portfolio — Most useful when your employer work is confidential: a small analysis on public or aggregated data, with the question and method written up, shows how you work without exposing anything you should not.

Skills worth including

Grouped rather than listed in one block. Grouping makes a long list readable and shows that you can tell the difference between the things you use daily and the things you have touched.

Beyond the technical list: Turning a vague request into a question the data can answer, Agreeing metric definitions with finance and operations, Saying when the data cannot support a conclusion, Writing findings for people who will not read the query. These belong inside your experience bullets, demonstrated, rather than in a list of adjectives.

Core analyst skills versus the wider tool stack

Analyst adverts often list ten or more tools, but the work and the interview usually test a much smaller core. Show the core through evidence, and name wider tools only when you used them for something you can describe. The split below is a working convention, not an official definition; analytics teams draw the line in different places.

The core: expected, and tested in interview

• SQL — a query or model other people rely on, with the constructs named (window functions, CTEs) and what it fed into.
• Spreadsheets (Excel or Google Sheets) — a model, reconciliation or recurring report someone else uses, and Power Query if you have used it.
• One BI tool in depth — a report in Power BI, Tableau or Looker Studio with a named audience and a refresh cadence.
• Data cleaning and reconciliation — a discrepancy you traced to its cause, and what you changed so it stopped recurring.
• Metric definitions and basic statistics — a definition you agreed with the business, or a readout where you judged a result inconclusive.

The ecosystem: name it when it is true and relevant

• Python or R — when you used it for real cleaning or analysis; name the libraries rather than the language alone.
• dbt — when you maintained models, not only ran them; tests and documentation are worth a mention.
• Cloud warehouses (BigQuery, Snowflake, Redshift) — when you worked in one; mirror the advert, since the concepts transfer but filters match names.
• A second BI tool — only if you built something in it that you could walk an interviewer through.
• Git — when your SQL or notebooks went through review, which says something about how your team worked.
• Orchestration (Airflow and similar) — if you scheduled or fixed pipelines yourself; otherwise it belongs on an analytics-engineering CV, not this one.

If most of your wider list is modelling libraries and deployment tools, you are probably describing data science work, and a data scientist CV will frame it better. An analyst CV that borrows data science vocabulary it cannot back up tends to be found out in the technical round.

Example professional summary

Three or four lines, positioned for the role rather than describing your personality. Two versions you can adapt:

Data analyst with four years in retail and logistics. Owns the weekly trading pack for a buying team, maintains the dbt models behind it, and writes the readouts for checkout and pricing tests, including the ones that come back inconclusive. SQL, Power BI and Excel daily; Python for cleaning and one-off analysis.

Analyst who is most useful where a business question is still vague: agreeing what the metric means, finding where the data disagrees with itself, and writing the answer so that someone outside analytics can act on it. SQL, Power BI, Excel and dbt.

Writing your experience

The difference between a CV that gets a call and one that does not is almost always in the bullet points. Each pair below shows a real rewrite of the kind of line that appears on most CVs in this field.

Taken from the example

The sample CV for this profession is fully written. A few sections from it, so you can see the level of specificity that works:

Experience

Data Analyst, Kirkstall Retail Group

• Took over the weekly trading pack for the buying team and replaced 23 tabs with a one-page exceptions view, so the Monday meeting now opens on the lines that moved.
• Rewrote the stock-cover query with window functions over daily snapshots instead of a self-join, which let the model refresh nightly rather than weekly.
• Found that store and online returns were recorded against different date fields, which made returns look seasonal; agreed a single definition with finance and added it to the data dictionary.
• Maintain 30 dbt models behind the trading and stock reports, with tests on keys and freshness that flag a failed load before the pack goes out.

Projects

• Allotment waiting-list analysis — Analysed five years of anonymised waiting-list records for a local allotment society to show which sites had the longest waits and why; the committee used it to decide where to split plots.

Education

• BSc (Hons) Mathematics and Statistics, University of Sheffield — Upper Second Class (2:1)

Certifications and registration

• Microsoft Certified: Power BI Data Analyst Associate (PL-300) — Microsoft
• dbt Fundamentals — dbt Labs

Common mistakes

ATS considerations

Applicant tracking systems behave differently by sector, and generic advice is often wrong for a given field. These points are specific to data analyst applications:

• Write "SQL" as a standalone term, not only inside a product name such as PostgreSQL, so that a plain keyword search has something to match.
• Use tool names exactly as the advert writes them - "Power BI" rather than "PBI", and the current product name if the advert uses it.
• Spell out "extract, transform, load (ETL)" once so both the phrase and the abbreviation are present.
• Keep key figures in the text, not inside an embedded chart image where a parser cannot read them.
• List Excel even if you also use SQL and Python. It still appears in many analyst adverts and is easy to leave out as too obvious.

The Minimal ATS layout is built for this, and the ATS guide covers what parsers do to a file in more detail.

Guides that go deeper

This page applies general CV advice to one field. The full treatment of each part is here:

• How to Write a Data Analyst CV — Every bullet point should end in a decision someone made differently. Dashboards with no audience are activity, not impact.
• How to Describe Work Experience — The difference between listing duties and demonstrating impact, and a repeatable way to rewrite any weak bullet point.
• How to List Skills on a CV — Group them, be specific, and cut anything you would not want to be interviewed on for ten minutes.
• How to Make an ATS-Friendly CV — What applicant tracking systems do to your file, the four failures that matter, and which of the usual claims about them anybody can actually stand behind.

Questions about data analyst CVs

Sources

• Microsoft Learn: Microsoft Certified: Power BI Data Analyst Associate (PL-300)(opens in a new tab) — checked 22 Sept 2026
• National Careers Service: Data analyst-statistician job profile(opens in a new tab) — checked 22 Sept 2026