Data analysis is a service role, and the CVs that work say so. The reviewer is not looking for someone who can produce a dashboard; they are looking for someone whose dashboards get used, whose numbers survive scrutiny from the finance team, and who can tell an operations manager something they did not already believe.
This has a direct consequence for how the CV is written. A bullet that ends with "built a dashboard" is incomplete. The same bullet ending with "which the regional managers now use in their weekly planning, replacing a manual spreadsheet" is a complete piece of evidence.
The second thing reviewers check is SQL depth, because it is the one skill that is used every single day and the one most often overstated. Naming the specific things you do in SQL - window functions, incremental models, query tuning - separates real practice from a course certificate.
This example is written for an analyst with four years of experience in a commercial analytics team.
Opens in the editor with this content already filled in, so you can replace it with your own.
Ananya Iyer
Data Analyst · SQL, Power BI & Commercial Analytics
ananya.iyer@example.com
+44 7700 900854
Leeds, United Kingdom
linkedin.com/in/example-ananya-iyer
Profile
Data analyst with four years in retail and subscription businesses. Work covers a regional dashboard used weekly by 40 store managers, a customer segmentation that corrected an 8% error in reported active accounts, and the readout on 11 pricing experiments. Strong SQL, Power BI and Python.
Professional Experience
Data AnalystJanuary 2023 – Present
Kirkstall Retail Group · Leeds, United Kingdom
•Built the regional performance dashboard now used weekly by 40 store managers, replacing a manually assembled spreadsheet that took a full day of analyst time each Monday.
•Segmented 1.2m customers by recency and frequency, which showed a third of "lapsed" accounts had simply changed payment method; correcting the definition raised reported active customers by 8% and changed the retention target for the year.
•Traced a persistent 4% variance between the sales ledger and the warehouse to duplicate order rows created by a retry in the ingestion job, and added deduplication plus a daily reconciliation check.
•Rebuilt the reporting layer in dbt with tests on 30 models, which cut the monthly close reporting cycle from four days to one.
•Ran the readout on 11 pricing experiments, including two where the recommendation was not to ship because the observed lift sat inside the confidence interval.
SQL · dbt · Power BI · Python · BigQuery
Junior Data AnalystSeptember 2021 – December 2022
Wharfeside Logistics · Bradford, United Kingdom
•Produced the daily operations report covering fleet utilisation and on-time delivery for a network of 180 vehicles.
•Automated six recurring Excel reports with Power Query, saving around 10 hours a week across the operations team.
•Investigated a rise in failed deliveries and found that two thirds were concentrated in three postcodes with an address-format problem in the routing import.
SQL · Excel · Power BI
Analytical Projects
Bradford food bank demand model
Volunteer analyst
•Built a simple demand forecast from two years of intake records to help a local food bank plan stock ahead of school holidays.
•Forecast held within 12% of actual weekly demand over the following six months; documented the limitations openly for the trustees.
Education
BSc (Hons) Mathematics and StatisticsSeptember 2018 – July 2021
University of Sheffield · Sheffield, United Kingdom
•Dissertation on seasonal adjustment methods for retail time series.
Upper Second Class (2:1)
Data Analyst example on the Data Professional layout. All details are fictional and shown for demonstration only.
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 at a specific level: joins are assumed, window functions and CTEs are the differentiator.
A named BI tool with the scale of what you built - number of users, refresh cadence, data volume.
Evidence of stakeholder work: who asked the question, what you told them, what they did.
Data quality work, which is most of the job and almost never appears on CVs.
Statistical judgement appropriate to the level: significance, sample size, and when not to draw a conclusion.
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.
Profile — Three or four lines positioning you for the role.
Technical Skills — Grouped skills, for example "Languages" and "Tooling".
Professional Experience — Paid roles, in reverse chronological order.
Analytical Projects — Work you built, with outcomes and the stack used.
Education — Degrees, diplomas and school-leaving qualifications.
Certifications — Completed certifications with the issuing body.
Languages — Spoken languages with CEFR levels.
Sections worth adding
Training — Useful when moving into analytics from another field and courses are recent.
Portfolio — A public dashboard or notebook helps a great deal when your commercial work is confidential.
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: Requirements gathering from non-technical stakeholders, Presenting findings to senior management, Saying when the data cannot answer the question, Training colleagues to self-serve. These belong inside your experience bullets, demonstrated, rather than in a list of adjectives.
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 subscription businesses. Work covers a regional dashboard used weekly by 40 store managers, a customer segmentation that corrected an 8% error in reported active accounts, and the readout on 11 pricing experiments. Strong SQL, Power BI and Python.
Commercial data analyst comfortable owning a question end to end: agreeing the definition with the business, building the model in dbt, and presenting the answer to people who will act on it. Four years across e-commerce and logistics.
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.
Weak
Created dashboards in Power BI.
Stronger
Built the regional performance dashboard now used weekly by 40 store managers, replacing a manually assembled spreadsheet that took a full day of one analyst’s time each Monday.
Names the audience, the cadence and the manual process it retired.
Weak
Analysed customer data.
Stronger
Segmented 1.2m customers by recency and frequency, which showed that a third of "lapsed" accounts had simply changed payment method; correcting the definition raised reported active customers by 8% and changed the retention target for the year.
A finding that overturned an assumption, with a downstream consequence.
Weak
Improved data quality.
Stronger
Traced a persistent 4% variance between the sales ledger and the reporting warehouse to duplicate order rows created by a retry in the ingestion job, and added a deduplication step and a daily reconciliation check.
Diagnosis, root cause and a preventative control - the shape of a real data quality story.
Weak
Supported A/B testing.
Stronger
Ran the readout on 11 pricing experiments, including two where the recommendation was not to ship because the observed lift was inside the confidence interval.
Demonstrates statistical discipline, which is rarer and more valuable than enthusiasm for testing.
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
Built the regional performance dashboard now used weekly by 40 store managers, replacing a manually assembled spreadsheet that took a full day of analyst time each Monday.
Segmented 1.2m customers by recency and frequency, which showed a third of "lapsed" accounts had simply changed payment method; correcting the definition raised reported active customers by 8% and changed the retention target for the year.
Traced a persistent 4% variance between the sales ledger and the warehouse to duplicate order rows created by a retry in the ingestion job, and added deduplication plus a daily reconciliation check.
Rebuilt the reporting layer in dbt with tests on 30 models, which cut the monthly close reporting cycle from four days to one.
Projects
Bradford food bank demand model — Built a simple demand forecast from two years of intake records to help a local food bank plan stock ahead of school holidays.
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
Dashboards with no audience
Building a report is effort; getting it adopted is impact. Say who uses it and how often, or the bullet reads as activity.
Overstating SQL
"Advanced SQL" is claimed on almost every analytics CV and tested in almost every interview. Name the constructs instead and let the reader grade you.
Course projects presented as experience
The Titanic dataset and the Superstore sample are instantly recognisable. If you have no commercial work yet, use a real dataset and a real question.
No mention of data quality
Cleaning, reconciling and chasing source-system problems is most of the working week. Leaving it out makes the CV read as theoretical.
Tool lists without depth
Power BI and Tableau and Looker and Qlik suggests four trials rather than one competence.
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 token. It is the single most-filtered term in analytics recruitment.
Include both "data visualisation" and "data visualization" is unnecessary - pick the local spelling - but do include the tool name, which is what filters actually match.
Spell out "extract, transform, load (ETL)" once so both the phrase and the abbreviation match.
Avoid embedding your key numbers in a chart image. Percentages should be in the text.
List Excel explicitly. Many analytics roles still screen for it and it is frequently omitted 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.
Questions about data analyst CVs
How much SQL do I need for a data analyst role?
Enough to write a multi-CTE query with window functions without help, and to work out why one is slow. Most interview tests sit exactly at that level.
Power BI or Tableau?
Whichever your target employers use. In UK and European corporate environments Power BI is more common; Tableau is more common in North American technology firms. Depth in one beats familiarity with both.
Do I need Python for a data analyst job?
Not always, but it widens the field considerably and is close to mandatory once you move towards data science. pandas at the level of cleaning and joining is enough to list it honestly.
How do I get experience without a job in analytics?
Use a real dataset with a real stakeholder - a local charity, a sports club, a small business - and write it up as a proper piece of analysis, including what you could not conclude.
Should I include a portfolio?
Yes if it contains original work with a stated question and a conclusion. A gallery of practice dashboards from tutorials does not help.