Data Analyst CV Example
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Data analysts don't get hired for knowing SQL; they get hired for turning data into a decision. Your CV needs to show the whole chain: what the data was, what you did with it, and what decision changed as a result.
Copy-ready professional summary
Copy it, then swap the numbers and details for your own.
Data analyst with four years analysing sales and operations data using SQL, Python and Power BI. Built 15 interactive dashboards now used daily by three departments, and a customer behaviour analysis that contributed to an 18% lift in retention.
Skills employers look for
- SQL — complex queries and optimisation
- Python — pandas and NumPy
- Power BI and Tableau
- Advanced Excel and modelling
- Statistics and hypothesis testing
- Data cleaning and quality assurance
Ready-made experience bullets
Replace anything in brackets with your own details, and keep the numbers — they're what sets your CV apart.
- Built 15 Power BI dashboards used daily by three departments for decision-making.
- Ran a customer behaviour analysis that drove a retention campaign, lifting retention 18%.
- Automated a weekly report that previously took eight hours of manual work.
- Cleaned and standardised a 1.2M-record database ahead of migration.
Education
BSc Statistics, Computer Science or Mathematics — [University].
ATS keywords
Include these terms exactly as written — applicant tracking systems match on literal text.
- SQL
- Python
- Power BI
- Tableau
- data analysis
- pandas
Tips specific to this role
Name the decision, not just the analysis
"Analysed sales data" describes a task. "Analysis that drove a campaign lifting retention 18%" is an achievement — and achievements are what get hired.
SQL goes first, always
It's the most in-demand and most filtered-on skill in this role. Put it at the top of your skills list and in the summary, not at the end of a line.
Data volume signals your level
"1.2M records" tells a hiring manager you've worked with real data, not a 300-row spreadsheet.
Licences and certificates employers ask for
Name in full any you hold, with the number where there is one — many postings screen on these.
- Microsoft Certified: Power BI Data Analyst Associate — the certification named most often in Saudi postings for this role.
- Google Data Analytics Professional Certificate — a good entry for career changers, and recruiters recognise it.
- Microsoft Certified: Azure Data Engineer or Fabric — if you are heading into data engineering rather than analysis.
- SAS or a statistics certificate if your sector is banking or healthcare, where SAS is still in use.
- ⚠️ A portfolio outranks all of them: three dashboards built on public data, each explaining the decision it supports.
Mistakes specific to this role's CV
- Naming tools without the decision. "Built dashboards" says nothing. "A dashboard that showed 30% of wastage sat in one branch, which was then closed" says everything.
- Leaving SQL at the end of the skills list. It is the first thing this role is filtered on and the thing you will be tested on in the interview.
- Omitting data volume. The gap between someone who handled a 500-row spreadsheet and someone who handled a million-row table is the entire job.
- Blurring data analyst, data engineer and data scientist. Three roles and three salaries — pick which you are applying for and do not claim all three.
- Showing beautiful dashboards with no business question behind them. Nobody is buying charts; they are buying an answer to a question that costs them money.
- Skipping data cleaning. Eighty per cent of the real work is there, and leaving it out reads as someone who has never touched real data.
Questions that come up in this role's interviews
Prepare an example from your own work for each — a generic answer is what every other applicant gives.
Write a query returning the top five customers by spend for each month.
Expect a practical SQL test in the interview. Revise window functions specifically — ROW_NUMBER and RANK with PARTITION BY — the most asked and most fumbled part.
Give me an example of an analysis that changed a decision.
This question is the whole job. Structure it: the question asked, the data you gathered, what you found, the decision taken, and the impact as a number. Stopping at "what you found" is not an answer.
How do you handle missing or unreliable data?
Do not say "drop the incomplete rows". Say you first find out why it is missing, then choose: drop, impute, or analyse separately — and that you state the gap in the report rather than hiding it.
What is the difference between correlation and causation?
A simple question that eliminates many. Answer with a real example from your work, and say what you did when asked for a causal explanation the data could not support — the right answer is that you said so.
How do you present a complex result to a non-technical board?
Lead with the finding and the recommendation, then the evidence, then the method for whoever asks. Anyone who starts with method loses the room in the first minute.
Where this job leads
From data analyst the road forks three ways: senior analyst then analytics manager — the track closest to the business — or data engineer if you lean toward infrastructure and pipelines, or data scientist if you lean toward modelling and machine learning. The second is the most in demand in Saudi Arabia today, because most organisations are still building their data foundations. What moves you is not another tool but becoming the person management asks before deciding — a reputation built on one well-timed, correct analysis.