Data analyst CV: example and tips

Keywords that come up in data analyst job ads

The role goes by many names: data analyst, business analyst, BI developer, reporting developer and, in Norwegian, dataanalytiker. Read what the job is actually about, and use the title and words from the ad where they match what you have done. Applicant tracking systems and recruiters look for the tools by name, so write “Power BI”, “SQL” and “Python” rather than “analytics tools”. Our guide to an ATS-friendly CV explains how these systems read your CV.

These words come up often in Norwegian ads for data analysts:

• SQL, Python or R for extracting, cleaning and analysing data.
• Power BI, Tableau, Qlik or Looker for reports and dashboards.
• Data modelling, DAX and data warehouses, for example Snowflake, BigQuery, Databricks or Azure.
• Statistics, A/B testing, forecasting and machine learning.
• Data quality, data governance and data protection.
• KPIs, insight and decision support for management and specialist teams.
• Communication, working with stakeholders and explaining numbers simply.

Formal requirements: education, certifications and data protection

There is no single route into the job. Many analysts have studied economics, statistics, mathematics or informatics, and some have a master's in business analytics, for example. Vocational colleges also run their own programmes, such as the 60 credit vocational degree in data analysis (Dataanalytiker) at Fagskolen Viken. Write your education with the full title and year, and include relevant courses or your master's thesis if you are early in your career.

Certifications are not required, but they can help when the ad names a specific tool. The Microsoft certification Power BI Data Analyst Associate (exam PL-300) must be renewed every 12 months, so give the year and whether it is still valid.

If you work with personal data, the General Data Protection Regulation (GDPR) applies, and it is Norwegian law through the Personal Data Act. Experience with anonymisation, access control and privacy by design is therefore worth mentioning, especially in the public sector, healthcare and finance.

Example of a summary for a data analyst CV

The summary should say what kind of analysis you do, which tools you are strongest in and what your analyses have led to. Here is a made up example you can use as a pattern:

Example: “Data analyst with four years of experience in retail and energy, strongest in SQL, Python and Power BI. Built the sales dashboard that 60 store managers use every week, and a forecasting model for ordering that cut waste in fresh food by 15 percent. Enjoys making complicated numbers simple for the people who have to make decisions. Looking for a role with more data modelling and A/B testing.”

Replace the numbers and tools with your own. Only write results you can explain, including how you measured them. You will find more examples in our guide to writing a CV summary.

Bullet points with results: weak versus strong

The most common weakness is bullet points that describe the tool instead of the value. “Built reports in Power BI” says what you did, but not why it mattered. Use a strong verb in the past tense, say which question the analysis answered, and finish with what happened next: time saved, money, fewer errors or a decision that was made.

• Weak: “Built reports in Power BI.” Strong: “Built a sales dashboard in Power BI for 60 stores, replacing twelve manual Excel reports and saving around 20 working hours a week.”
• Weak: “Worked with SQL.” Strong: “Wrote SQL extracts and data models in Snowflake that halved the time from new data arriving to reports being updated.”
• Weak: “Analysed customer data.” Strong: “Found through a cohort analysis that customers who used the app in their first week stayed twice as long, which formed the basis for a new welcome campaign.”
• Weak: “A/B testing.” Strong: “Planned and analysed 14 A/B tests in the online shop, where three of the changes together raised conversion by 0.8 percentage points.”
• Weak: “Responsible for data quality.” Strong: “Introduced automated checks of customer data, reducing errors in the address field from 9 to 2 percent.”
• Weak: “Presented to management.” Strong: “Presented the quarterly analysis to the management team, who used it to move 3 million kroner of the marketing budget to the channels with the best return.”

Skills, tools and portfolio

Group your skills so the recruiter can see the tools quickly. Only include what you can use on your own. If you have little experience, a small portfolio can show more than a long list: two or three analyses of open data, for example from Statistics Norway (SSB), with code, a dashboard and a short explanation of what you found. This belongs in the skills section:

• Query languages and programming: SQL, Python with pandas, or R.
• Visualisation: Power BI with DAX, Tableau, Looker or Qlik.
• Data and platforms: Snowflake, BigQuery, Databricks, dbt, Azure or advanced Excel.
• Methods: statistics, regression, forecasting, A/B testing and segmentation.
• Certifications with name and year, for example PL-300 for Power BI.
• Industry knowledge, for example retail, banking, energy or healthcare.

Recent graduate, career change or long experience

If you have just finished your studies, your master's thesis, course projects and summer jobs are often the strongest things you have. Say which data you used, which method and what you found, for example “analysed 200,000 trips from open city bike data and found which stations are most often empty in the morning”. Our guide to a CV for recent graduates has more advice.

If you are switching from another field, such as finance, marketing or logistics, your domain knowledge is a strength. Show how you have already used data in your job, for example reports you have built or processes you have sped up with Excel or SQL. With many years behind you, your CV should show more about ownership and influence: which decisions your analyses supported, who you worked with, and whether you have built up a team or a data platform. Cut older roles down to one line.

Common mistakes in a data analyst CV

Look out for these mistakes before you send your CV:

• Tools without results. What changed because you did the analysis?
• Numbers without context. “Increased by 15 percent” must say what increased, and from what.
• Jargon only analysts understand. The first reader is often a recruiter.
• Skill levels shown as stars or percentages.
• Screenshots of dashboards with real customer data or internal figures in your portfolio.
• The same CV for a BI role and a machine learning role.

Tailor your CV to every analyst job with Selja

Analyst jobs look alike in name but not in content. A BI role wants Power BI, DAX and data modelling, another wants Python, statistics and A/B testing. With Selja you paste in the job ad and upload the CV you already have. In under a minute you get a professional, ATS-friendly CV and cover letter tailored to the job, with the tools and words from the ad where they match what you have done. That way you can apply for more jobs without rewriting your CV every time.

Selja never adds tools or experience you do not have. If the ad asks for dbt, for example, and your CV does not show it, Selja asks you first and only uses what you confirm. If you want to see where you stand first, take a free ATS check against the ad or check your CV for free. With Pro you also get Job match, which gives you a score from 0 to 100 for how well you fit the job. See pricing, or get started.

The application, the case task and pay

In your cover letter, show that you understand which questions the business wants answered. Mention a concrete example of an analysis that led to a decision, and link it to what they are looking for. Keep the letter to one page. See our example of a job application and our guide on how to write a cover letter.

Many employers set a case task, for example a dataset you have to analyse and present. Practise explaining your findings simply and saying what you would recommend. Use the interview questions to prepare, and check the figures in the pay check before you negotiate.