Data analyst CV example
A data CV is read for decisions triggered, not for a tool list: what you measured, what changed in the business, and with which stack.
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- Sector
- Technology and data
- Template used
- Berlin
- ATS level
- ATS compatible
- ATS keywords
- 5
Document preview
How the CV looks once laid out
The same example content, laid out in the recommended template: single column, selectable text and no tables or graphics that break automated parsing.
Sample document. Open it in the editor to change template, language and sections without losing the content.
Mark Vickers
Data analyst with SQL, Python and dbt modelling
Professional summary
Data analyst with five years in e-commerce and fintech. I built the company data model in dbt on BigQuery, cut churn with a propensity model and replaced 40 manual reports with Looker dashboards. Advanced SQL plus Python for analysis and automation.
Experience
Data analyst
Employer name · City · 2019 – Present
- Cut monthly churn by 2.3 points with a propensity model actioned through CRM
- Replaced 40 manual reports with 6 dashboards, saving 80 hours a month
- Modelled 120 tables in dbt with data quality tests and auto documentation
Education
Qualification or certificate for the role
Training centre · Year
01
Professional headline
This is the line that decides whether they keep reading: a specific role and years of experience, never an adjective.
Data analyst with SQL, Python and dbt modelling
02
Example professional summary
Three or four sentences with your specialism, one figure and your goal. It sits right under the headline.
Data analyst with five years in e-commerce and fintech. I built the company data model in dbt on BigQuery, cut churn with a propensity model and replaced 40 manual reports with Looker dashboards. Advanced SQL plus Python for analysis and automation.
03
Achievements that work in this role
Past-tense verb, what you did, measurable result. Three to five per role, strongest first.
- 01Cut monthly churn by 2.3 points with a propensity model actioned through CRM
- 02Replaced 40 manual reports with 6 dashboards, saving 80 hours a month
- 03Modelled 120 tables in dbt with data quality tests and auto documentation
04
Skills to include
Written as plain text: rating bars are invisible to automated filters.
- Advanced SQL
- Python (pandas)
- dbt and BigQuery
- Looker and Power BI
- A/B testing and statistics
- Dimensional modelling
05
ATS keywords
The terms filters search for in this role. Place them inside your experience, not in a list at the end.
- SQL
- Python
- dbt
- BigQuery
- dashboard
Work them naturally into your experience — these are the terms screening software scans for.
06
Tips for this CV
What separates a CV for this trade from a generic one.
- 01
Give each achievement the decision it drove and its figure, not just the analysis.
- 02
Separate languages, warehouses and BI tools; ATS filters match each name on its own.
- 03
Link a repo or case study in the header — in data it always gets checked.
07
What a data analyst CV is
Short definition, ready to quote.
A data analyst CV is the one or two page document used to apply for a data analyst role in technology and data: it brings together the professional headline, the profile summary, experience with measurable results and the competencies the trade expects you to evidence, such as Advanced SQL and Python (pandas). Unlike a generic CV, it is ordered around what the applicant tracking system and the hiring manager look for in a data analyst: the words used in the advert and the numbers of the actual job.
08
Keywords and where to place them
Each term works best in a specific place in the document. Repeating them all at the end scores nothing.
| Keyword | Where to place it |
|---|---|
| SQL | In the professional headline |
| Python | In the profile summary |
| dbt | Inside an achievement with a figure |
| BigQuery | In the skills list |
| dashboard | In training or certifications |
09
How this data analyst CV gets through the filters
What the example above does right, point by point.
- The headline reads «Data analyst with SQL, Python and dbt modelling»: the job title (data analyst) appears literally in the first line, not as a creative synonym.
- The trade's keywords —SQL, Python, dbt— sit inside the experience and summary, where the filter reads them in context.
- Every achievement carries its figure. «Cut monthly churn by 2.3 points with a propensity model actioned through CRM» is the pattern to copy: volume, timeframe and result.
- Skills such as Advanced SQL, Python (pandas), dbt and BigQuery are written as plain text, never as rating bars or stars, which no parser can read.
- The Berlin template is two-column with a linear reading order and rated ATS compatible, so the sections come out in order.
10
What hiring managers look for in a data analyst
The three points that separate two candidates in this trade.
The decision your analysis changed
Analysts are not hired for charts but for the decisions they caused. Write the case: what was decided from your analysis and what moved afterwards, in revenue, retention or hours saved.
SQL, modelling and BI tool
SQL is the language of the job: say whether you write window functions, CTEs and optimised queries, and at what data volume. Add modelling (dbt, star schema) and the tool (Power BI, Tableau, Looker) with dashboards in production.
Data quality
This is what separates a reliable analyst: validation, duplicate control, documented metric definitions and agreement with the business on what each one means. A dashboard without agreed definitions never gets used.
11
Common mistakes on a data analyst CV
The ones that repeat in this role, each with its fix.
- 01
Listing tools with no use case
“Power BI, SQL, Python” says nothing alone. Each tool belongs inside an analysis with its business question and its outcome; anything else is a course inventory.
- 02
Confusing reporting with analysis
Publishing a dashboard is delivery; analysis is forming a hypothesis, testing it and recommending. A CV that only lists reports positions you as a chart producer, the lowest band in the field.
- 03
No volume and no sources
Three thousand rows in a spreadsheet is not thirty million in a warehouse. Give the volume, the number of integrated sources and the refresh frequency.
- 04
Ignoring stakeholder communication
Analysis nobody understands never gets applied. Mention presentations to leadership, training dashboard users, and how you compress a technical result into a one-sentence recommendation.
12
What a data analyst earns
So you can tell whether an advert is at market rate before applying.
- Usual range
- Typically £35,000-£55,000 a year, with analytics engineering and modelling-heavy roles above
- What moves the figure
- Sector and data scale move the figure more than years served.
- Where the figure comes from
- Ranges observed in UK data analyst adverts through 2026.
Frequently asked
Questions about a data analyst CV
- SQL or Python — which matters more?
- SQL, clearly, because it is the daily tool and the focus of the technical test. Python follows for automation and modelling; strong SQL plus business judgement gets hired ahead of Python with no judgement.
- Is a data portfolio worth building?
- It helps a great deal and acts as an early technical test: two or three analyses on public data, each with the question, method and conclusion. Reasoning quality matters more than chart polish.
- How do I show impact when data is confidential?
- In relative terms: percentage improvement, hours saved a month, decisions supported. Never absolute company figures, and nothing that identifies clients or pricing.
- Do Power BI or Google certificates help?
- As a baseline signal, not as a substitute for experience. Include them with the year, but let the CV rest on real analyses and their outcomes.
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