International CV template for Google Docs™ (A4)
A4, no photo, a professional summary at the top and room for two pages. Written the way UK and European employers expect a CV.
- Parser read 63/63 fields
- Up to 2 pages
- US Letter and A4
- Google Docs and Word
- Arial
What a parser reads from it
Field by field, from the PDF that Google Docs exports.
63/63
fields read correctly from the resume (A4), downloaded from Google Docs as a PDF and read by the open-source OpenResume parser (commit 4f8255a) on Oct 1, 2026.
What the parser read
First 30 lines, exactly as extracted.
Oliver Hughes Manchester, UK | +44 7700 900456 | oliver.hughes@email.com | linkedin.com/in/oliverhughes PROFESSIONAL SUMMARY Data analyst with six years of experience in retail and energy, most recently leading pricing and customer analytics for a UK retail group. I build models and dashboards that commercial teams use every week, and I enjoy explaining numbers to people who don't work with data all day. Looking for a senior analyst or analytics lead role in Manchester or remote. WORK EXPERIENCE Northgate Retail Group Manchester, UK Senior Data Analyst Mar 2023 – Present • Lead a team of three analysts supporting pricing, promotions and loyalty for 140 stores and the online shop • Built a promotion uplift model in Python that replaced a manual spreadsheet process and is now used to plan every seasonal campaign • Designed the weekly trading dashboard in Power BI, used by 60 managers across buying and store operations • Found that 18% of loyalty vouchers were going to customers who would have bought anyway, and redesigned targeting to save about £400,000 a year • Introduced code review and a shared SQL library, which halved the time new analysts need to deliver their first report • Worked with the supply chain team on a markdown model for seasonal stock, reducing end-of-season clearance by about 9% across clothing and homeware Pennine Energy Leeds, UK Data Analyst Jun 2020 – Feb 2023 • Forecast monthly churn for 250,000 household customers and flagged at-risk accounts for the retention team • Built a self-serve churn dashboard so the retention team could filter at-risk customers by tariff and region without waiting for an analyst • Automated regulatory reporting to Ofgem with SQL and Python, cutting a three-day monthly task to two hours
Field by field
What we wrote, and what the parser put in each field.
| Field | Parser read | Match |
|---|---|---|
| Name | Oliver Hughes | |
| oliver.hughes@email.com | ||
| Link | linkedin.com/in/oliverhughes | |
| Summary | Data analyst with six years of experience in retail and energy, most recently leading pricing and customer analytics for a UK retail group. I build models and dashboards that commercial teams use every week, and I enjoy explaining numbers to people who don't work with data all day. Looking for a senior analyst or analytics lead role in Manchester or remote. | |
| Job 1 company | Northgate Retail Group | |
| Job 1 title | Senior Data Analyst | |
| Job 1 dates | Mar 2023 – Present | |
| Job 1, bullet 1 | Lead a team of three analysts supporting pricing, promotions and loyalty for 140 stores and the online shop | |
| Job 1, bullet 2 | Built a promotion uplift model in Python that replaced a manual spreadsheet process and is now used to plan every seasonal campaign | |
| Job 1, bullet 3 | Designed the weekly trading dashboard in Power BI, used by 60 managers across buying and store operations | |
| Job 1, bullet 4 | Found that 18% of loyalty vouchers were going to customers who would have bought anyway, and redesigned targeting to save about £400,000 a year | |
| Job 1, bullet 5 | Introduced code review and a shared SQL library, which halved the time new analysts need to deliver their first report | |
| Job 1, bullet 6 | Worked with the supply chain team on a markdown model for seasonal stock, reducing end-of-season clearance by about 9% across clothing and homeware | |
| Job 2 company | Pennine Energy | |
| Job 2 title | Data Analyst | |
| Job 2 dates | Jun 2020 – Feb 2023 | |
| Job 2, bullet 1 | Forecast monthly churn for 250,000 household customers and flagged at-risk accounts for the retention team | |
| Job 2, bullet 2 | Built a self-serve churn dashboard so the retention team could filter at-risk customers by tariff and region without waiting for an analyst | |
| Job 2, bullet 3 | Automated regulatory reporting to Ofgem with SQL and Python, cutting a three-day monthly task to two hours | |
| Job 2, bullet 4 | Worked with the call centre to analyse complaint reasons, which led to a simpler bill layout and 15% fewer billing calls | |
| Job 2, bullet 5 | Ran A/B tests on renewal emails that raised fixed-tariff renewals by 6 percentage points | |
| Job 3 company | Calder & Wright | |
| Job 3 title | Graduate Analyst | |
| Job 3 dates | Sep 2018 – May 2020 | |
| Job 3, bullet 1 | Cleaned and analysed sales data for consulting projects with clients in food manufacturing and logistics | |
| Job 3, bullet 2 | Built Excel models for market sizing and supplier cost comparisons used in client recommendations | |
| Job 3, bullet 3 | Prepared charts and short summaries for partner presentations, often on tight overnight deadlines | |
| Job 3, bullet 4 | Interviewed purchasing managers at eight food manufacturers to check our cost assumptions, and wrote up the findings for the client steering group | |
| Job 4 company | University of Sheffield Department of Economics | |
| Job 4 title | Research Assistant | |
| Job 4 dates | Jul 2017 – Aug 2018 | |
| Job 4, bullet 1 | Collected Land Registry and census data for a study on regional house prices | |
| Job 4, bullet 2 | Cleaned and matched 1.2 million sale records to local authority areas | |
| Job 4, bullet 3 | Wrote Stata scripts for the regression analysis and documented each step | |
| Job 4, bullet 4 | Produced the maps and charts used in the final report and a journal article | |
| Job 4, bullet 5 | Checked other researchers' code before each draft went to the funder | |
| Job 4, bullet 6 | Presented interim results at two department seminars | |
| Job 5 company | Harlow's Pharmacy | |
| Job 5 title | Sales Assistant | |
| Job 5 dates | Sep 2015 – Jun 2017 | |
| Job 5, bullet 1 | Worked weekend shifts on the till and pharmacy counter while studying, and trained four new starters | |
| Job 5, bullet 2 | Kept the weekly stock count for the health and beauty aisles and flagged slow lines to the store manager | |
| School 1 | University of Manchester | |
| School 1 degree | MSc Data Science, Distinction | |
| School 1 dates | Sep 2019 – Sep 2021 | |
| School 1 GPA | (empty) | |
| School 1, bullet 1 | Part-time while working; dissertation on forecasting store footfall with weather and calendar data | |
| School 2 | University of Sheffield | |
| School 2 degree | BSc (Hons) Economics, First Class | |
| School 2 dates | Sep 2014 – Jun 2017 | |
| School 2 GPA | (empty) | |
| Project 1 | Store footfall forecasting | MSc dissertation, Python | |
| Project 1 dates | Jan 2021 – Sep 2021 | |
| Project 1, bullet 1 | Combined two years of door-counter data with weather and bank holiday calendars to forecast daily footfall for 40 stores | |
| Project 1, bullet 2 | Cut forecast error by 22% against the retailer's existing method, and the model became the starting point for staffing plans at Northgate | |
| Project 2 | Open data dashboard for Leeds bus punctuality | Personal project, Power BI | |
| Project 2 dates | Mar 2022 | |
| Project 2, bullet 1 | Built a public dashboard from open bus timing data that a local transport campaign group used in its submission to the council | |
| Skills line 1 | Analysis: SQL, Python (pandas, scikit-learn), R, Excel | |
| Skills line 2 | Reporting: Power BI, Tableau, Looker Studio | |
| Skills line 3 | Data platforms: Snowflake, BigQuery, dbt, Git | |
| Skills line 4 | Languages: English (native), German (intermediate) | |
| Skills line 5 | Certifications: Microsoft Power BI Data Analyst Associate |
Shown but not counted
- +44 7700 900456: outside the parser's US-format rules. It's in the extracted text.
- Manchester, UK: outside the parser's US-format rules. It's in the extracted text.
- Manchester, UK: no location field for jobs. It's in the extracted text.
- Leeds, UK: no location field for jobs. It's in the extracted text.
- Leeds, UK: no location field for jobs. It's in the extracted text.
- Sheffield, UK: no location field for jobs. It's in the extracted text.
- Sheffield, UK: no location field for jobs. It's in the extracted text.
- Manchester, UK: no location field for schools. It's in the extracted text.
- Sheffield, UK: no location field for schools. It's in the extracted text.
This checks our sample text, read by one open-source parser from the PDF that Google Docs' File > Download > PDF produces. It isn't a score, and it doesn't tell you how Workday, Greenhouse or any other hiring system will read your own resume. Anyone can rerun it: the test repository has the PDF (SHA-256 2f2d914abf8e…), the expected values and the raw results. How we test
Specs and editing tips
Everything you need to edit it without breaking the layout.
Specs
- Font
- Arial, installed on Windows and macOS, so the .docx keeps its look
- Body text
- 10.5 pt
- Margins
- 15 mm (A4), 0.6 in (Letter)
- Length
- Up to 2 pages with the sample content
- Sections
- Professional Summary, Work Experience, Education, Skills
- Good for
- 3+ years, outside the US
- Layout
- One column, no tables, text boxes, images or icons. Dates aligned with a right tab stop.
Editing tips
- Dates stay on the right by themselves. Each heading line has a right-aligned tab stop. Press Tab before the date instead of adding spaces.
- Add a job by copying one. Select a whole entry, heading lines and bullets, copy it and paste it below. The spacing comes with it.
- Ran onto a second page? Set margins to 0.5 in, body text to 10.5 pt and line spacing to single, in that order. More ways to fit one page
- Rename your copy. Recruiters see the file name, so use something like Firstname-Lastname-Resume.
Exporting your finished resume
Two downloads from Google Docs cover almost every application.
File > Download > PDF Document. This is the exact path we test on every release. It gives selectable text with the fonts embedded.
Word
File > Download > Microsoft Word (.docx). Our own .docx files are exported this way, and we check that text and bullets survive. Don't convert a PDF to Word.
Similar templates
Same rules, a different look.
Compact
36/36 fields readHalf-inch margins and tight spacing for people with more experience than one page normally holds.
- 1 page
- Letter + A4
- Docs + Word
Plain
33/33 fields readNothing but text, left-aligned, in Arial. A safe default for any job when you don't want the layout to be noticed.
- 1 page
- Letter + A4
- Docs + Word
Modern Accent
35/35 fields readOne ink-blue accent on headings and rules, with a summary up top. Clean enough for any parser, with a little more personality.
- 1 page
- Letter + A4
- Docs + Word
Questions
- Is it really free?
- Yes. No account, no email and no watermark. Copy it, edit it and send it to as many employers as you like. The one thing you can't do is repost the template file itself somewhere else.
- Will it get through an ATS?
- Nobody can promise that, and we don't. What we can show is that an open-source parser reads our sample of this template correctly, field by field, from the PDF Google Docs produces. Workday, Greenhouse and others use their own parsers, and your own content matters as much as the layout. How we test
- Can I use it in Microsoft Word?
- Yes. Download the .docx, or copy to Google Docs and later use File > Download > Microsoft Word. The font is installed on Windows and macOS, so nothing gets swapped.
- Why no photo, icons or two columns?
- Parsers read a page top to bottom. Columns, text boxes and icons are the usual reason a resume comes out scrambled. US employers also generally don't want photos.
- Can my name have accents or non-Latin characters?
- Yes, with one caveat. In our tests the parser read José Álvarez, Mary-Jane Smith, Sean O'Brien and Иван Петров correctly, and the layout didn't move. A Chinese name like 王小明 also kept the page intact, but Google Docs swaps in a Japanese font for those characters and the parser left the name field empty. For US applications, put the romanized name first, like Xiaoming Wang (王小明). Phone numbers outside the US format, like +44 7700 900123, aren't picked up as phone numbers either.