Technology

Data Analyst Resume Example (2026): Template, Skills and Summary

By EZProCV Editorial Team · Updated

This data analyst resume example gives you a complete sample resume, summaries for entry-level, mid-level and senior analysts, bullet rewrites, and the skills and ATS keywords that appear again and again in data analyst job ads. An ATS (applicant tracking system) is the software employers use to collect and search applications.

Hiring managers reading a data analyst resume look for two things: technical fluency with SQL, spreadsheets and a BI tool, and business sense. The best bullets show a question, the analysis and the decision that followed.

Copy the structure below, swap in your own numbers and tools, and use the regional notes if you're applying in the UK, the Gulf or India.

Use this resume template — freeSee the example

Quick answer: A strong data analyst resume shows the decisions your analysis changed, not just the tools you used. Open with a summary naming your core tools (SQL, Excel, Python or R, Tableau or Power BI) and your business domain, then write bullets that link each analysis to a result such as revenue, cost, time saved or forecast accuracy. Keep it to one page and link a portfolio.

Data Analyst resume example

A complete, ATS-friendly example. Names and companies are fictional.

Mei Lin Chen

Data Analyst, Retail and E-commerce

Chicago, IL, USA · meilin.chen@example.com · +1 555 0136 · linkedin.com/in/meilinchen-example · github.com/meilinchen-example

Summary

Data analyst with 7 years of experience turning sales, marketing and supply-chain data into decisions for retail and e-commerce teams. Expert in SQL, Python and Power BI; built the self-serve KPI suite now used by 140 managers each week. Known for clear storytelling with data and for analyses that led to a $2.3M reduction in markdown losses.

Experience

Data Analyst · Lakeshore Home Goods

Chicago, IL · Jan 2022 – Present

  • Built a markdown-optimization model in Python and SQL across 9,000 SKUs that cut end-of-season markdown losses by $2.3M (14%) in its first year.
  • Designed 12 Power BI dashboards on a Snowflake star schema, replacing 30 manual weekly Excel reports and saving the merchandising team about 25 hours a week.
  • Ran A/B test analysis for 40+ site experiments, including a free-shipping threshold change that raised average order value by 9%.
  • Wrote dbt models and data-quality tests that cut dashboard data errors reported by users from about 15 a month to 2.
  • Presented monthly category performance reviews to the VP of Merchandising, translating findings into three pricing changes per quarter on average.

Junior Data Analyst · Prairie Freight Services

Milwaukee, WI · Jul 2019 – Dec 2021

  • Analyzed carrier performance data for 2,500 weekly shipments in SQL and Tableau, identifying routes that reduced late deliveries by 18%.
  • Automated a weekly fuel-cost report with Python and the Excel API, cutting preparation time from 6 hours to 20 minutes.
  • Built a demand forecast in Excel and R that improved staffing accuracy at two warehouses, lowering overtime costs by 11%.
  • Cleaned and merged three legacy customer databases (180,000 records), removing 22,000 duplicates before a CRM migration.

Projects

  • Chicago Bike-Share Demand: Public Tableau dashboard and Python notebook modeling trip demand by weather and season using open city data.

Education

B.S. Statistics, minor in Economics, University of Illinois Chicago, Chicago, IL · 2019

Skills

SQL (Snowflake, PostgreSQL, BigQuery) · Python (pandas, NumPy, statsmodels) · R · Excel (Power Query, pivot tables, XLOOKUP) · Power BI and DAX · Tableau · dbt · A/B testing and hypothesis testing · Regression and forecasting · Data cleaning and validation · Data modeling (star schema) · Google Analytics 4 · Data storytelling · Stakeholder management

Certifications

  • Microsoft Certified: Power BI Data Analyst Associate
  • Google Data Analytics Professional Certificate

Languages

English (Native) · Mandarin Chinese (Fluent)

Build yours from this template

Data Analyst resume summary examples

Entry-level

Recent economics graduate with a Google Data Analytics certificate and hands-on SQL, Excel and Tableau experience from a six-month internship analyzing customer churn for a telecom provider. Built three portfolio projects on public datasets and enjoys turning messy data into clear, practical recommendations.

Mid-level

Data analyst with 4 years of experience in marketing analytics. Uses SQL, Python and Looker to measure campaign performance across a $6M annual budget, and built the attribution reporting that helped shift 20% of spend to higher-return channels.

Senior

Senior data analyst with 9 years of experience leading analytics for finance and operations. Owns KPI definitions for a 3,000-person company, manages a team of three analysts and partners with executives on planning, forecasting and pricing decisions.

Data Analyst resume bullet points: weak vs strong

WeakStrong
Created reports for the sales team.Built an automated Power BI sales dashboard used daily by 60 reps, replacing a manual Excel report and saving 10 hours a week.
Analyzed customer data.Segmented 250,000 customers by purchase behavior in SQL and Python, informing a retention campaign that cut churn by 7%.
Used SQL to pull data.Wrote and optimized 80+ SQL queries in BigQuery, cutting average query run time by 60% and monthly compute costs by $4,000.
Worked on forecasting.Built a weekly demand forecast in Python that reduced forecast error (MAPE) from 22% to 12% and cut stockouts by 15%.
Cleaned data.Designed validation rules and deduplication scripts that raised CRM data accuracy to 98% before a Salesforce migration.
Presented findings to management.Presented quarterly pricing analysis to the leadership team, leading to two price changes that added $850K in annual gross margin.

Skills for a data analyst resume

Hard skills

  • SQL querying and optimization
  • Advanced Excel
  • Data visualization and dashboard design
  • Statistical analysis
  • A/B testing
  • Forecasting and regression
  • Data cleaning and validation
  • Data modeling
  • Python or R for analysis
  • KPI definition and reporting
  • ETL and data pipelines
  • Business requirements gathering

Soft skills

  • Data storytelling
  • Business acumen
  • Critical thinking
  • Attention to detail
  • Stakeholder communication
  • Curiosity
  • Time management

Tools and software

  • Microsoft Excel
  • Power BI
  • Tableau
  • Looker or Looker Studio
  • Snowflake, BigQuery or Redshift
  • Python (Jupyter, pandas)
  • dbt
  • Google Analytics 4
  • Git
  • Jira

ATS keywords for data analyst resumes

Use the terms that appear in the job ad you're applying to — exactly as written — wherever they truthfully describe your experience. These come up most often for this role:

  • data analyst
  • data analysis
  • SQL
  • Excel
  • Python
  • R
  • Tableau
  • Power BI
  • data visualization
  • dashboards
  • reporting
  • KPIs
  • statistical analysis
  • A/B testing
  • ETL
  • data cleaning
  • data modeling
  • business intelligence
  • forecasting
  • stakeholder management
  • Snowflake
  • BigQuery
  • Google Analytics
  • data-driven insights

How do you write a data analyst resume summary?

Your summary should answer three questions in three or four sentences: what domain you analyze, which tools you use, and what your work changed.

Data analyst with 3 years of experience in healthcare operations. Uses SQL, Excel and Tableau to track patient flow across five clinics, and built the wait-time dashboard that helped cut average waits by 19 minutes.

Marketing data analyst skilled in SQL, Python and GA4. Measures campaign return across paid, email and organic channels and built the attribution model now used for quarterly budget planning.

Name your domain if you have one (retail, finance, healthcare, logistics). Domain knowledge is often what separates two candidates with the same tools.

How do you write data analyst bullet points with impact?

Use the question → analysis → decision → result pattern. Every bullet should show that someone did something differently because of your work.

  1. Start with a strong verb: analyzed, built, automated, forecast, segmented, modeled.
  2. Name the data and tools: "250,000 transactions in SQL and Python."
  3. Show the decision or result: revenue gained, cost avoided, hours saved, error reduced.

O*NET's profile for business intelligence analysts lists tasks such as creating business intelligence tools and generating standard or custom reports; if you do this work, quantify how many reports, how many users and how much time they saved. Avoid bullets that only list tools ("used Excel, SQL and Tableau") with no outcome.

Which skills and tools should a data analyst list?

Group your skills so a recruiter can confirm the essentials quickly:

Group What to include
Querying SQL dialects you know (PostgreSQL, BigQuery, Snowflake)
Spreadsheets Excel or Google Sheets: pivot tables, Power Query, lookups
Programming Python (pandas) or R
Visualization Power BI, Tableau, Looker
Statistics A/B testing, regression, forecasting
Business KPI design, requirements gathering, storytelling

Recruiters commonly screen for SQL and Excel first, so make both easy to find. List only tools you can use in a technical test; many employers give a SQL or Excel exercise early in the process.

Should a data analyst include a portfolio or projects?

Yes, especially if you're entry-level or changing careers. A short Projects section proves you can take raw data to a conclusion.

Good portfolio projects:

  • use a real public dataset (city open data, government statistics, sports data)
  • ask a clear business-style question
  • show your SQL or Python code and a dashboard
  • end with a recommendation, not just charts

Link a GitHub repository, Tableau Public profile or personal site in your header, and describe one or two projects in a line each. For more layout guidance, start from the free Modern data science template in EZProCV and download it as a text-based PDF.

What does the data analyst job market look like?

The US Bureau of Labor Statistics doesn't publish a separate "data analyst" occupation; analyst roles are spread across several groups. For the closely related data scientists occupation, BLS reports a median annual wage of $120,230 in May 2025 and projects 35% employment growth from 2025 to 2035, much faster than average. Data analyst pay is usually lower than data scientist pay and varies by industry and city.

In the UK, the National Careers Service publishes a data analyst-statistician profile, and the government's Digital and Data Profession Capability Framework defines data analyst skills by level. Both are useful for matching your resume language to what employers expect.

How a data analyst resume changes by country

US & Canada

One page for most analysts, no photo or personal details. Lead with SQL, Excel and your BI tool. Certifications such as Microsoft Power BI or Google Data Analytics help career changers. See the USA resume format and Canada resume format.

UK & Ireland

Use CV and a short personal statement; two pages is acceptable. Public-sector roles often follow the government's data analyst skills framework, so mirror its terms (data visualization, data ethics, statistical methods). See the UK CV format.

Gulf (UAE, Saudi Arabia, Qatar)

Add nationality, visa status, notice period and often a photo. Banking, telecom, government and energy employers are major analytics hirers; highlight Arabic and English reporting and any experience with large-scale transformation programs. See the UAE resume format.

India

State total experience, current location, notice period and core tools in the first lines. Freshers should list internships, certifications and projects with links. Leave out photo, date of birth and family details unless asked. See the India CV format.

Common data analyst resume mistakes

  • Listing tools without results. "Proficient in Tableau" means little next to "built a Tableau dashboard used by 80 managers."
  • Hiding SQL. Recruiters search for it first; put it in your summary, skills and at least one bullet.
  • Using charts or graphs inside the resume. They look clever but ATS software can't read them.
  • Forgetting the business context. Say what the analysis was for and what changed afterward.
  • Overclaiming machine learning. If you've only run a tutorial model, list it as a project, not a core skill.
  • Ignoring data quality work. Cleaning, validating and documenting data is valuable; quantify it.

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Frequently asked questions

A header with links to LinkedIn and a portfolio, a short summary, grouped skills (SQL, Excel, a BI tool, Python or R, statistics), reverse-chronological experience with quantified bullets, education and relevant certifications. Entry-level candidates should add a Projects section with two or three complete analyses.
Lead with education, certifications and two or three projects built on real public data, each with a question, the tools used and a clear finding. Add internships, research assistant work or analysis you did in another job, such as tracking sales in Excel. Keep it to one page and link your GitHub or Tableau Public profile.
It can help, particularly for career changers and entry-level candidates who need proof of skills. Vendor certifications such as Microsoft Power BI Data Analyst or Tableau credentials are useful when the job uses that tool. List them in a separate section with the issuer and year; experience and projects still matter more.
Only if you can use it in a short technical test. Be honest about level: "Python (pandas, basic scripting)" is better than a bare "Python" that invites advanced questions. Show it in a project or bullet, such as automating a report, so the claim has evidence behind it.
A data analyst resume centers on SQL, reporting, dashboards and business decisions. A data scientist resume puts more weight on statistics, machine learning, experimentation and production models. If you're moving toward data science, see the data scientist resume example for how to present modeling work.
One page is enough for most analysts with under about 7 years of experience. Senior and lead analysts who manage people or own company-wide KPIs can use two pages. Cut tools you haven't used recently and older bullets that don't relate to the job you want.

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