What Hiring Managers Really Look for in a Data Analyst CV

This is an expert guest post written by Jacob Wickett from Live Digital.

The stories are true, hiring managers often take just seconds to review a CV. According to Novorésumé’s 2025 Hiring Landscape survey, 42% of recruiters only take under 10 seconds to review résumés in the initial stage. Have you ever wondered what they’re looking for? 

If you’re applying for roles that require highly technical skills, this is a consideration worth looking into. Understanding the recruitment from a hiring manager’s perspective is an advantage you can use to put yourself in front of the line.  

For data analysts, what do you think could make your CV not only stand out, but also more compelling?  

Read on to find out just that! 

Common data analyst CV mistakes that turn off hiring managers 

Too often, hiring managers run into tropes when screening CVs, which causes them to move on immediately. Never overdo specific areas of your CV. Maintain a balance between analytical and personable, and avoid the following CV mistakes: 

• Generic experience descriptions: Saying “worked with data” without context, tools, or outcomes shows a lack of clarity and impact. 

• Overused buzzwords: Phrases like “results-driven” or “innovative problem solver” without real, concrete examples come across as fluff. 

• Lack of data-driven insights: No measurable results and failing to show how your work improved efficiency, reduced costs, or drove business decisions weakens credibility. 

• Ignoring ATS requirements: Using graphics, tables, or unusual fonts can make your CV unreadable by applicant tracking systems. 

• Too much technical jargon: Listing every tool without explaining how you used them will only look like a keyword dump. 

• Missing a professional summary: Skipping this section makes it harder for hiring managers to quickly understand your profile. 

• Unfocused or unrelated experience: Including unrelated job history without linking it to transferable skills can dilute your CV. 

• Typos or formatting issues: Sloppy presentation can signal poor attention to detail, something no data analyst should lack. 

This top-rated Quora answer explains some of these turn-offs:

When applied to data analyst CVs, many applicants get rejected because they fail to show how they think or solve problems, not because they actually lack potential. Hiring managers need to see evidence of data-driven outcomes that relate to your actual business impact. 

How to write a CV for an entry-level or junior data analyst position 

Entry-level candidates and junior data science professionals should focus on potential, initiative, and practical skills in lieu of experience. 

Hiring managers will expect candidates to be able to interpret and analyze data and demonstrate an aptitude for this. Highlight this on your CV using relevant coursework and final-year projects that you were involved in. Any data-driven decision-making should be outlined. 

Relevant academic work, bootcamp projects, data challenges (e.g. Kaggle) or your personal portfolio/GitHub should also be included. Ensure emphasis lies upon tasks such as data cleaning, simple data manipulation, and a learning mindset. 

Scopely’s Director of Data Science answered the following question on Quora, which I think is insightful: 

Landing a job without experience doesn’t only require patience. You need to show your potential, and that you have what it takes to be an ideal employee. For entry levels, a promising candidate is someone who has foundation skills. In data analyst roles, this means having at least a basic grasp of statistics and exposure to programming languages. It’s a bonus to have the initiative and motivation to learn. 

Tailoring your CV for data analyst roles: Start with the job description 

Tailoring your CV shows that you understand the role, especially when applying for niche positions like a marketing data analyst or business data analyst. The easiest and most effective way to find the right keywords is to read the job description. Start by highlighting skills, tools, and responsibilities mentioned by the employer.  

Job descriptions will often use industry-specific terms and language. For example, in marketing roles, employers often mention “customer segmentation,” “Google Analytics,” and “A/B testing,” which are core skills for any aspiring marketing data analyst. 

Along with keywords and industry-specific jargon, a data-driven CV should reflect how the candidate has contributed to solutions that align with company goals. Potential employers should get an accurate picture of a candidate’s previous work experience and how it aligns with the data analyst role. 

Using the right keywords from the job description is one way to beat the applicant tracking system and get seen.  

What hiring managers want to see in a data analyst CV 

Before you start reviewing data analyst interview questions and answers to make sure you ace the interview, remember that your CV is what will help you make sure you get your foot in the door in the first place. A CV is a representation of a candidate, and during the job application process, candidates need to communicate their best qualities clearly to allow hiring managers to understand exactly who they are. 

Hiring managers have busy schedules and often have an overwhelming number of applications to sift through. They are looking for clarity, relevance, and measurable impact. They want to be able to glance at a CV and pinpoint relevant data analytics keywords and quantified achievements in previous roles in the data analysis field.   

Recruiters often spend just seconds scanning a CV. This means they inevitably focus on job titles, recent experience, and key skills that they can spot in the top third of the page. Additionally, spelling and grammar mistakes often reveal a lack of attention to detail, a skill imperative in data analysis. 

This statement from a hiring manager at Spiceworks sums it up perfectly. Hiring managers don’t have time to dig for potential.  

CV formats that hiring managers prefer 

One big sticking point with CV writers and candidates is format and design. Unfortunately, no matter how beautiful some designs may look, they can impede an applicant tracking system’s ability to process a CV.  

So, it’s important to focus on data analyst CV formats that appeal to both humans and applicant tracking systems (ATS). Fancy graphics, images, and columns should sit on the back burner.  

Here are some CV formatting tips to follow: 

• Professional summary: A short outline of the experience, specializations ,and unique qualities you can bring to the role. Remember to use industry-specific jargon that fits naturally within this section.  

• List key technical and soft skills: List out (using bullet points) data analysis tools you know how to use, like SQL, Python, and Power BI, along with soft data analysis skills like communication and problem solving. 

• Structure experience: Detail previous roles in bullet point format and structure them in reverse chronological order to display the most recent experience first.  

• Education and relevant certifications: Mention degrees, relevant boot camps, and certifications that have enabled you to perform past data analyst roles skillfully.  

• Projects or portfolio links: Showcase real examples of your work using GitHub links, dashboards, or case studies. 

Unfortunately, ATS often rejects fancy visuals or tables before a human gets to see them. Qualified candidates may inadvertently push themselves out of the running with a CV ill-formatted for ATS.  

This Instagram post from Login360 sums up the tips you need to make your CV ATS-friendly. If the ATS can’t read it, it won’t matter how great the design looks. 

 

A standout data analyst CV summary 

Writing a compelling data analyst CV summary that grabs attention is fairly simple as long as candidates follow a few guidelines. Professional summaries are typically 2-4 sentences long. 

As potential employers spend just seconds scanning a CV with a bigger focus on the top third of the page, a summary should be hyper-relevant to the role and include tools that show your technical fluency.  

• Example of a strong summary: “Junior data analyst with experience in e-commerce, skilled in SQL, Python, and Tableau. Improved customer retention by 12% through behavioral trend analysis. Strong foundation in statistical modelling and data visualization.” 

• Example of a weak summary: “Hardworking and passionate individual looking for a senior data analyst role. Fast learner and team player.” 

In the above examples, the latter is too generic and lacks personality and information. No data analyst skills, relevance, or even the role were mentioned in the weak summary. Meanwhile, the first example mentioned the role, relevant tools, and even quantified impact.  

This TikTok by The Introverted Recruiter, Lee Harding, makes the point well: a CV summary isn’t the place for fluff. Hiring managers want to see clear value straight away.  

@theintrovertedrecruiter How to write a Professional Summary on your CV #jobsearch #jobseeker #jobsearchtips #jobseekers #careeradvice #cv #resume #recruitment #recruiting ♬ original sound – Lee Harding | Career Advice

Technical skills that hiring managers want to see 

More often than not, hiring managers want to see a particular set of technical skills in experienced data analyst CVs, including data analysis software, data processing, and data mining skills. Some of the most important skills are listed below: 

• Programming Languages: 
  • Python (for data cleaning, analysis, and automation) 
  • SQL (for querying and managing databases) 
  • R (for statistical analysis and visualization) 
• Data Analysis & Data Visualization Tools: 
  • Excel (pivot tables, VLOOKUP, dashboards) 
  • Power BI, Tableau (interactive dashboards and reports) 
  • Looker, Google Data Studio (web-based visualization) 
• Database Management & ETL Tools: 
  • MySQL, PostgreSQL, Microsoft SQL Server 
  • BigQuery, Snowflake 
  • Apache Airflow, Alteryx (for ETL and workflow automation)  
• Statistical Analysis & Machine Learning: 
  • Scikit-learn, statsmodels, TensorFlow (basic ML models, regression, clustering) 
  • A/B testing, hypothesis testing, forecasting models  
• Data Modeling & Warehousing: 
  • Star and snowflake schemas 
  • Data lakes and warehousing solutions, like Redshift or Azure Synapse  
• Version Control & Collaboration: 
  • Git, GitHub (for code management and collaboration) 

Showcase technical expertise by mentioning tools in the context of previous experience and link these tools to results achieved and quantifiable outcomes.  

A good example of this is: 

“Automated a weekly Excel report using Python, reducing manual effort by 8 hours per week.”  

This example is much more effective than simply saying “Python” or “Reported using Python weekly.” 

Soft skills that make skilled data analysts stand out 

Soft skills can be just as important for data analysts. Highlighting these on your CV can make the difference between being invited for an interview and not.  

Here is a comprehensive list of interpersonal traits that hiring managers typically want to see in skilled data analysts: 

• Communication skills: 
  • Ability to explain complex data in plain language. 
  • Example: “Presented actionable insights to marketing leadership, leading to a pivot in campaign strategy.”  
• Storytelling with data: 
  • Turning raw numbers into meaningful narratives that drive decisions. 
  • Example: “Created a visual dashboard highlighting user churn drivers, influencing product roadmap priorities.” 
• Collaboration with cross-functional teams: 
  • Working closely with product, finance, marketing, or engineering teams is especially useful in a financial data analyst role, where accuracy and cross-functional alignment are critical. 
  • Example: “Partnered with developers to define key data metrics for new app features.” 
• Stakeholder management: 
  • Understanding business needs and tailoring analysis accordingly. 
  • Example: “Ran weekly syncs with sales leads for iteration on KPI dashboards.” 
• Critical thinking & problem solving: 
  • Approaching ambiguous problems with analytical skill and reasoning. 
  • Example: “Identified inconsistent metrics in revenue reports, traced root cause to data pipeline errors.”
• Attention to detail: 
  • Ensuring accuracy in data cleaning, reporting ,and visualization. 
  • Example: “Caught and corrected a data type mismatch skewing financial model results.” 
• Adaptability & learning agility: 
  • Quickly picking up new tools, frameworks, or business contexts. 
  • Example: “Learned Power BI to deliver client-ready reports on time. 
• Influence & persuasion: 
  • Using data to support a recommendation or change. 
  • Example: “Used cohort analysis to persuade leadership to shift acquisition budget toward higher-retention segments.”  

Knowing Python or SQL is expected, but being able to explain results, collaborate, and think critically is what actually drives actionable business strategies and builds trust with stakeholders. 

Hiring managers want results, so show your impact 

When applying for a data analyst position, it’s important to quantify your impact. A standout data analyst CV focuses on results rather than simple tasks. Shifting from duties to results signifies a candidate’s impact and their role in achieving company success. To do this, try listing your experience with action verbs.  

Use words like “developed”, “improved”, “analyzed”, “automated,” or “optimized” to start bullet points. Avoid phrases such as “responsible for creating reports.” 

• Strong Example: “Built a customer data segmentation model using Python and K-means clustering, leading to a 20% increase in targeted email campaign engagement.” 

• Weak Example: “Worked with data for the marketing team.” 

Using action verbs can help you frame your achievements and showcase your work on a business value level. Include examples where you were able to save time, improve results, improve accuracy, or drive revenue. 

Final thoughts 

Every data analyst candidate should be able to land an interview using the steps outlined in this article. Just keep in mind to present a professional, relevant, and successful persona , and you’ll find no trouble reaching your goals. Best of luck!