Data Analyst CV Example
A data analyst CV is screened by a hiring manager, analytics lead, or recruiter looking for three proofs before anything else: that you can pull and shape data (SQL plus a scripting language), that you can turn it into something a business will act on (dashboards, models, clear recommendations), and that your past work moved a real metric. Analytics hiring has conventions that generic CV advice misses. Tooling is checked literally — SQL, Python or R, and a BI platform like Power BI, Tableau, or Looker are hard requirements rather than nice-to-haves, and reviewers scan for the exact named tools their stack uses. A portfolio or a set of concrete projects carries as much weight as years of experience, because it is the fastest way for a reviewer to verify you can actually do the work. And the bullets that win are the ones that quantify: a line that says 'built dashboards' loses to 'rebuilt the retention dashboard in Looker, cut reporting lag from 3 days to 15 minutes, and surfaced a churn driver worth £140k a year'. This example covers the structure that surfaces those signals in the order data reviewers look for them, the technical-skills block that does the verification work, the summary and portfolio sections that prove capability, the experience bullets that win shortlists, and the common mistakes that drop strong analysts below the cut. Everything is editable in the Cvida builder — use it as a starting point and tailor it for your domain, your stack, and the seniority of the role you are targeting.
Why a data analyst CV is different from a generic CV
Analytics hiring runs on signals most generic CV advice ignores. Start with what makes it different:
- Tooling is verified, not assumed: SQL is the baseline, then a scripting language (Python or R) and a BI platform (Power BI, Tableau, Looker, Qlik) — list the exact tools the role names and reviewers will check them against the job description before they decide to interview
- A portfolio proves what bullets only claim: a public GitHub, a Tableau Public profile, or two or three documented projects let a reviewer confirm in two minutes that you can clean, query, model, and visualise real data
- Quantification is the gold standard: revenue influenced, hours of manual reporting automated away, decision latency cut, model accuracy gained, cost saved — vague 'analysed data' bullets read as filler
- Domain context matters: e-commerce, fintech, healthcare, SaaS, and marketing analytics each have their own metrics (LTV, churn, CAC, cohort retention, funnel conversion) — naming the ones you have owned signals you understand the business, not just the query
- Communication is part of the job spec: stakeholder management, turning analysis into a recommendation, and presenting to non-technical audiences separate analysts who get hired from ones who only get filtered
Treat your CV as the hiring manager's shortcut to a yes. An analytics lead reading it should be able to confirm your stack, see proof of real work, and read at least one quantified outcome inside two minutes — and if they can't, you don't make the shortlist no matter how good you'd be once you're in the role.
The CV structure that works for data analyst roles
Most data analyst CVs land best in this order — it front-loads the signals analytics reviewers look for first:
- Header: name, professional title (e.g. 'Data Analyst — SQL, Python, Power BI'), city / region, email, phone, LinkedIn, and a portfolio or GitHub link
- Summary (3–4 lines): years of experience, domain, core stack, and one headline quantified outcome
- Technical skills: grouped by type — querying (SQL), languages (Python / R), BI and visualisation, data modelling / warehousing, statistics — so reviewers can scan for a match in seconds
- Experience: reverse-chronological roles with employer + sector + team context, 4–6 outcome-focused bullets each
- Projects / portfolio: two or three concrete pieces with the problem, the tools, and the measurable result — essential for early-career and career-changer candidates
- Education: degree subject + institution, plus any quantitative or analytics-heavy modules
- Certifications: Google Data Analytics, Microsoft Power BI (PL-300), Tableau Desktop Specialist, cloud data certifications, and relevant coursework
Keep it to 1 page for under 5 years of experience, 2 pages once you're a senior analyst or analytics lead with people or major projects under you. Put the portfolio link in the header so it survives even a 10-second skim.
The fundamentals of CV structure and length this example builds onThe technical skills block: SQL, Python, BI, and statistics
This is where reviewers decide whether you can do the job from day one or need months of ramp-up. Group it so a busy reader can scan it in seconds:
- Querying: SQL — specify dialects and depth (window functions, CTEs, query optimisation across Postgres, MySQL, BigQuery, Snowflake, Redshift)
- Languages: Python (pandas, NumPy, scikit-learn, matplotlib / seaborn) or R (tidyverse, ggplot2, Shiny) — name the libraries you actually use, not just the language
- BI and visualisation: Power BI (DAX, Power Query), Tableau, Looker / LookML, Qlik, Looker Studio
- Data modelling and pipelines: dbt, dimensional modelling, ETL / ELT, Airflow, data-warehouse design, version control with Git
- Statistics and methods: A/B testing, regression, hypothesis testing, forecasting, cohort and funnel analysis, segmentation — the analytical methods behind the tools
List the tools the job description names first — reviewers and ATS keyword filters both check for an exact match. Don't pad the list with tools you've touched once; a technical interviewer will probe anything you claim, and a thin claim costs you more than an honest omission.
How to choose and present the skills that actually move a CVThe summary: domain, stack, and a quantified outcome
Three or four lines at the top of the page. It should answer what kind of analyst you are, what you work in, and one result that proves you deliver:
- Line 1: title + years + domain. Example: 'Data Analyst with 5 years in e-commerce and subscription analytics.'
- Line 2: core stack + scale context. Example: 'Daily SQL and Python across a 40-million-row warehouse in BigQuery; owner of the company's Looker reporting layer.'
- Line 3: headline outcome with a number. Example: 'Built a churn-prediction model that flagged at-risk accounts 30 days early and informed a campaign that recovered £210k in ARR.'
- Line 4 (optional): what you're targeting. Example: 'Seeking a Senior Data Analyst role on a product or growth team where analysis drives roadmap decisions.'
- What to drop: 'detail-oriented', 'passionate about data', 'fast learner' — every applicant claims these; a number and a named tool do the persuading
A summary that names a domain, a stack, and a measurable result beats one full of adjectives every time. If you can't put a number in line 3 yet, lead with a portfolio project instead — proof of work is more convincing than a self-description.
How to write CV achievements that quantify in money, time, or riskPortfolio and projects: proving you can do the work
For analysts, a portfolio is often the deciding factor — especially for first roles and career changers. It turns 'I can do this' into 'here's the proof'. Each project should show:
- The question: the real business or research problem you set out to answer, in one plain sentence
- The data and tools: where the data came from, how you cleaned and modelled it, and which tools you used (SQL, Python, the BI layer)
- The method: the analysis itself — the query, the model, the test — described so a technical reviewer can judge the rigour
- The visual: a dashboard, chart, or notebook a reviewer can actually open — host it on Tableau Public, GitHub, or a personal site
- The result: what the analysis revealed and, where possible, what decision or change it drove
Two strong, well-documented projects beat ten half-finished ones. Pick problems close to the domain you're targeting, write each up as a short case study, and link them from the header so a reviewer never has to hunt. A portfolio that mirrors the role's real work is the closest thing to a free trial you can offer.
Experience bullets: the quantification that wins shortlists
This is where most analyst CVs go flat — they describe tasks instead of impact. Rewrite every bullet around a result a business cares about:
- Lead with the outcome, then the method: 'Cut weekly reporting time by 12 hours by automating a manual Excel process in Python and Power BI' beats 'Responsible for weekly reports'
- Put a number in most bullets: revenue influenced, % efficiency gained, hours saved, error rate reduced, users / rows / queries at scale — even an estimate with a stated basis beats none
- Name the tools inside the bullet: '…in SQL and dbt', '…with a Looker dashboard', '…using a scikit-learn model' — it doubles as keyword coverage for ATS
- Show the stakeholder: who used the analysis and what they did with it — 'gave the growth team a funnel breakdown that lifted checkout conversion 8%'
- Use strong, specific verbs: built, automated, modelled, forecasted, segmented, optimised — not 'helped with', 'worked on', or 'was involved in'
A reviewer skims bullets in seconds. If the first half of each line carries a verb and a number, you survive the skim — and an analyst who quantifies their own work is exactly the analyst a hiring manager wants quantifying theirs.
How to format bullets and keywords so they clear ATS filtersEducation, certifications, and the analyst learning path
Analytics is one of the most credential-flexible fields in tech — degrees, bootcamps, and self-taught portfolios all get hired. Present whichever route you took with confidence:
- Degree: subject + institution + classification, and call out quantitative modules (statistics, econometrics, computer science, mathematics) even if the degree itself isn't 'data'
- Certifications that carry weight: Google Data Analytics Professional Certificate, Microsoft PL-300 (Power BI Data Analyst), Tableau Desktop Specialist, dbt, AWS / Azure / GCP data certifications
- Bootcamps and courses: name the programme, the capstone project, and the tools covered — a documented capstone is worth more than the certificate alone
- Self-taught path: lead with the portfolio and the platforms you've completed (DataCamp, Kaggle competitions, freeCodeCamp) — Kaggle rankings are concrete, verifiable proof
- Keep it current: list certifications with the year earned and prune anything outdated, so the section reads as active, ongoing learning
No single route wins on its own — what convinces is the combination of a credential and proof you can apply it. A self-taught analyst with two strong portfolio projects often beats a degree-holder with no demonstrable work.
Common mistakes that drop strong analysts below the cut
Even capable analysts get filtered for avoidable reasons. Check yours against this list before you apply:
- Listing tools without depth or proof: a wall of logos with no projects or quantified bullets reads as keyword-stuffing, and the technical interview exposes it fast
- Describing tasks, not impact: 'created reports', 'analysed data', 'maintained dashboards' — no number, no stakeholder, no result
- Hiding or omitting the portfolio: for analysts it's often the single most persuasive asset, yet it's missing or buried below the fold
- One generic CV for every role: not mirroring the job description's named stack (Power BI vs Tableau, Python vs R) costs you both the ATS match and the human skim
- Over-engineering the design: dense infographics, skill 'percentage' bars, and rainbow charts hurt readability and break ATS parsing — clean structure and real numbers win
Most of these come down to one habit: showing impact and proof instead of listing duties and tools. Fix that and you clear the bar that filters out the majority of applicants before a human even reads closely.
More tactics for tech and data CVs, ATS, and technical screensFinal notes and the hiring-manager test
Before you send it, run your CV through the same quick test an analytics hiring manager will:
- Stack check: can they confirm SQL, a scripting language, and a BI tool in the first 10 seconds?
- Proof check: is there a portfolio or project link they can open, and does it work?
- Impact check: does at least one bullet per role carry a number tied to a business outcome?
- Match check: does the CV mirror the named tools and domain in the job description?
- Readability check: clean structure, no skill bars, parses cleanly as plain text for ATS?
If you can answer yes to all five, your CV does its job — it gets you into the room where your actual analysis can speak for itself. Build and tailor yours in the Cvida editor, swap in your domain and stack, and lead with the numbers that prove your work.