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7 AI Data Engineer - Data Analytics CV Templates with Example

By Ria · Last Updated On 23 October 2026
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This guide shows you how to create an AI Data Engineer - Data Analytics CV that is clear, focused and easy to review. It offers practical advice to help you organise your evidence and choose a format that supports your goals.

A strong CV for this role makes data preparation, analysis and visualisation easy to understand. Use clear sections and focused bullet points to show how you work with data and share useful findings.

Compare the example presented in 7 layouts through PNG image previews, then open a chosen example in the editor to customise it. The guide also covers ATS readability, useful CV sections and relevant terms, including Excel, SQL, Python, Tableau, Microsoft Power BI and the four Microsoft credentials.

What Makes a Great AI Data Engineer - Data Analytics CV in 2026

A strong CV gives data preparation, analysis and dashboard work a clear place. Focused bullets help reviewers see your contribution and the result of each project.

Use relevant terms such as SQL query optimisation, Python data cleaning, data validation, Tableau and Microsoft Power BI. Add figures only when you can support them with evidence.

Note: CV examples are for educational purposes only. Use only information that accurately reflects your experience and qualifications. References to real organisations do not imply affiliation or endorsement.

#1 Simple One Column Format CV Example

The Simple One Column Format places experience, skills and education in one sequence, giving reviewers a direct route through the CV.

 
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Why This Format Works

  • Experience, education and skills follow one sequence, giving career details a clear reading path.
  • Project achievements can be reviewed in order without shifting between parallel columns.
  • Supporting sections remain part of the same continuous reading sequence.

Best For

  • Linear career histories
  • Detailed project records
  • CVs with several evidence sections
  • Sequential reviewer preferences
  • Education alongside practical projects

CV Keywords

  • Wrote and optimised SQL queries
  • Python
  • clean and manipulate inconsistent product and transaction datasets
  • Microsoft Power BI
  • Tableau
  • Excel
  • Dashboard development
  • Azure AI Fundamentals (AI-901)
  • Power BI Data Analyst (PL-300)
  • AI Business Professional (AB-730)
  • Microsoft 365 Copilot & Agent Administration Fundamentals (AB-900)
  • Completed instructor-led online training and practical exercises in a Microsoft lab environment
  • Data analytics
  • portfolio development
 

#2 Simple Two Column Format CV Example

The Simple Two Column Format separates CV content into two reading areas, helping reviewers scan supporting details alongside the career record.

 
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Why This Format Works

  • Separate columns let reviewers compare experience with supporting sections at a glance.
  • Education and credentials can sit apart from longer project and experience entries.
  • Parallel reading areas help keep skills visible alongside the career record.

Best For

  • Side-by-side section review
  • CVs with varied evidence
  • Experience paired with supporting details
  • Skills requiring easy access
  • Project and education records

#3 Plain One Column with Shaded Labels CV Example

Plain One Column with Shaded Labels keeps the CV in one reading sequence, making it straightforward to follow experience and supporting sections.

 
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Why This Format Works

  • A single reading sequence keeps experience entries in their intended order.
  • Education and credentials can be found without switching between content columns.
  • Project details remain connected to the wider career record in one flow.

Best For

  • Chronological experience records
  • Connected project details
  • Single-route CV review
  • Education and credentials together
  • Readers who prefer linear layouts
 

#4 One Column Format with Photo Right CV Example

One Column Format with Photo Right combines a single-column CV with a photo, keeping the content in a direct sequence while including a portrait.

 
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Why This Format Works

  • Experience and project entries read in sequence, helping reviewers follow development.
  • Skills and education remain part of the same direct content route.
  • Supporting credentials are easy to locate within a single-column CV.

Best For

  • CVs including a portrait
  • Sequential career records
  • Project details with supporting sections
  • Direct content review
  • Education and skills in one sequence
 

#5 Clean Two Column Sidebar Format CV Example

Clean Two Column Sidebar Format uses two reading areas to set apart parts of the CV, helping reviewers scan career details and supporting information.

 
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Why This Format Works

  • Two reading areas help distinguish career history from supporting CV sections.
  • Experience entries can retain a clear sequence within their own column.
  • Separate content areas give skills and credentials space beside career details.

Best For

  • Distinct content areas
  • Career history with supporting sections
  • CVs with several skill groups
  • Experience entries needing clear order
  • Quick access to credentials
 
 

#6 Two Column Format CV Example

Two Column Format arranges CV content in parallel areas, offering reviewers a way to scan career details alongside other sections.

 
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Why This Format Works

  • Parallel columns give project work and supporting sections distinct reading areas.
  • Reviewers can scan skills separately while keeping the career record in view.
  • Education and credentials remain easy to locate without interrupting experience entries.

Best For

  • Parallel section review
  • Project work with supporting details
  • Skills beside career history
  • CVs with varied content
  • Education and credentials alongside experience
 

#7 Photo Header Two Column Format CV Example

Photo Header Two Column Format combines two reading areas with a photo, giving the CV a portrait while keeping its sections distinct.

 
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Why This Format Works

  • Career entries can be followed in one column while other sections remain visible.
  • Education and credentials have a separate area from detailed experience entries.
  • Skills are easier to scan without breaking the order of project records.

Best For

  • Two-column CVs including a portrait
  • Separate career and supporting sections
  • Detailed experience with related records
  • Skills that need quick access
  • Project history with education
 
 
 

Why Build Your AI Data Engineer - Data Analytics CV with ResumeInMinutes

ResumeInMinutes helps you arrange skills, projects, education and experience in a clear CV. Compare PNG image previews, then open an example in the editor to customise its content and layout.

  • Different CV layouts
  • Clear section structure
  • Space for role-specific keywords
  • Organised skills and project evidence
  • PNG image previews
  • CV customisation in the editor

Choose the format that makes your evidence easiest to follow. Keep the focus on data preparation, well-documented analysis and clear dashboards.

 

See Also

 

Frequently Asked Questions

Are these AI Data Engineer - Data Analytics CV templates ATS-friendly?

The examples use clear headings and sections to support scanning. Include relevant terms naturally, such as SQL query writing, Python data cleaning, Excel analysis, Tableau and Microsoft Power BI.

Are these separate CV examples or different layouts of one example?

This page shows one role-focused CV example in different layouts. Compare the PNG image previews, then open a preferred example in the editor to customise it.

What should an AI Data Engineer - Data Analytics CV include?

Include a focused summary, relevant skills, education, credentials and project or experience evidence. Explain how you prepare and check data, write SQL queries, build dashboards and communicate findings.

Which CV layout works well for this role?

Choose a layout with clear headings and an easy-to-follow order for skills, experience, projects and education. A simple structure can help recruiters and applicant tracking systems find key details.

How can I show data analytics achievements on my CV?

Use concise bullet points to explain the task, your approach and the result. For example, describe how you checked records, investigated data patterns or made dashboard findings easier to understand. Include figures only when they are accurate and supported.

Should I include a photo on my CV?

For UK applications, a photo is usually not needed. Use the space for relevant skills, project evidence and clear results instead.

ATS score and CV checks

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ATS Score 100%

How we calculate this score

  • Keywords: 60% of the total. AI extracts keywords from your job description. If you do not provide one, a standard job description for the title matching your CV is used. This is not a match to a specific vacancy.
  • Text-based CV: 20%. The text CV assessed here receives 100 for this part.
  • CV quality: 20%. We start at 100 and subtract points for issues found by our CV checks, down to a minimum of zero.
  • Keyword coverage = round(100 × confirmed matches ÷ total keywords). Exact matches and supported equivalents verified by AI receive full credit; unsupported keywords receive none. Live text checks use the saved equivalents and do not request a new AI review. Without keywords, no overall ATS score is available.
  • Final score = 60% of keyword coverage + 20% of the text score + 20% of the quality score, rounded to a whole number. Without keywords, no overall ATS score is available.

What this score means

Our scores are estimates, not employer ratings. They may contain errors and do not guarantee accurate parsing, a ranking, an interview, or a job. Check your document against the employer’s requirements. You are responsible for relying on this score.

Terms and Conditions

Score weights: Keywords 60% · Text based CV 20% · CV quality 20%

Keyword coverage: 14 of 14 (100%) · CV quality: 100%

CV quality 100% · Weight 20% · Issues (0)

Details
  • ✓Name present: Andrew Morris
  • ✓Email format: andrew.morris@example.com
  • ✓Phone format: 07700 90XXXX
  • ✓Experience 1: Job title: AI Data Engineer, Portfolio Project
  • ✓Experience 1: Company name: Applied AI Portfolio Project
  • ✓Experience 1: Work location: Remote
  • ✓Experience 1: Employment dates: May 2026 - Present
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: Data Analytics Capstone Project
  • ✓Experience 2: Company name: University of North Trent
  • ✓Experience 2: Work location: Nottingham, Nottinghamshire
  • ✓Experience 2: Employment dates: Sep 2023 - Jun 2024
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: Data Reporting Student Lab
  • ✓Experience 3: Company name: University of North Trent
  • ✓Experience 3: Work location: Nottingham, Nottinghamshire
  • ✓Experience 3: Employment dates: Jan 2023 - Aug 2023
  • ✓Experience 3: Description or achievements: Present
  • ✓No placeholder fields
  • ✓No repeated achievement bullets
  • ✓No empty bullets

No issues found by the current checks.

Text Based CV 100% · Weight 20%

Parsed text
Andrew Morris
07700 90XXXX
andrew.morris@example.com
andrewmorris
Leeds, West Yorkshire, LS6 2AB
SUMMARY
Early-career AI Data Engineer with a BSc in Business Information Systems and a practical portfolio in data preparation, analysis, visualisation and introductory AI. Use Excel, SQL and Python to prepare and query datasets, then present findings through Tableau and Microsoft Power BI dashboards. Completed instructor-led training and live Microsoft lab exercises, applying mentor feedback to portfolio development and project documentation. Hold four Microsoft credentials spanning data analysis, Azure AI, AI business applications and Copilot administration, and communicate findings and data limitations clearly to non-technical audiences.
EDUCATION
BSc Business Information Systems
2024
University of North Trent
Nottingham, Nottinghamshire
TECHNICAL SKILLS
Programming Languages: Python, SQL, DAX
Spreadsheets: Microsoft Excel, Power Query M, Excel formulas
Business Intelligence: Microsoft Power BI, Tableau, Tableau Prep
Microsoft Data Platforms: Microsoft Fabric, Azure Synapse Analytics, Azure Data Lake Storage
Azure AI: Azure AI Foundry, Azure Machine Learning, Azure AI Search
Microsoft 365: Microsoft 365 Copilot, Microsoft Excel, Microsoft SharePoint
Data Formats: CSV, JSON, XML
Relational Databases: Microsoft SQL Server, PostgreSQL, MySQL
Python Data Libraries: pandas, NumPy, Matplotlib
Development Tools: Visual Studio Code, Jupyter, Git
Data Connectivity: ODBC, JDBC, REST
SKILLS
SQL query writing
Python data cleaning
Data validation
Excel analysis
Data analytics
Dashboard development
Data visualisation
Portfolio development
AI use case review
Microsoft lab exercises
Query optimisation
Dataset manipulation
Report writing
Stakeholder communication
Interview preparation
EXPERIENCE
AI Data Engineer, Portfolio Project
May 2026 - Present
Applied AI Portfolio Project
Remote
Developed a small retail sales portfolio project, using Python to clean and manipulate inconsistent product and transaction datasets before loading prepared tables for analysis.
Wrote and optimised SQL queries to check joins, summarise sales by product and period, and investigate anomalies; documented query logic so results could be reproduced and reviewed.
Created complementary Tableau and Microsoft Power BI dashboards, selecting visualisations that helped users compare sales patterns and identify incomplete records.
Completed instructor-led online training and practical exercises in a Microsoft lab environment, using mentor feedback to refine project documentation and prepare the portfolio for interviews.
Applied learning from Microsoft Azure AI Fundamentals and Microsoft AI Business Professional modules to assess suitable introductory AI use cases, and reviewed Microsoft 365 Copilot concepts through the Microsoft 365 Copilot & Agent Administration Fundamentals module.
Data Analytics Capstone Project
Sep 2023 - Jun 2024
University of North Trent
Nottingham, Nottinghamshire
Built an analysis of a simulated public transport dataset as a final-year capstone, using Excel and SQL to reconcile records and investigate service patterns.
Produced a Tableau dashboard and a Microsoft Power BI report for a student review panel, explaining the differences between the two views and documenting assumptions behind each measure.
Presented findings and data limitations in a concise written report, translating technical observations into practical options for a non-technical audience.
Data Reporting Student Lab
Jan 2023 - Aug 2023
University of North Trent
Nottingham, Nottinghamshire
Prepared coursework datasets in Excel and Python, correcting inconsistent categories and missing values before analysis in supervised lab sessions.
Used SQL queries to filter and aggregate sample records, then checked outputs against source tables to catch errors before submitting assignments.
Practised fundamental data visualisation and written interpretation, incorporating tutor feedback to make charts clearer and conclusions appropriately qualified.
PORTFOLIO
Title: Retail Sales Data Analytics Portfolio
Link: Portfolio project, no URL supplied
Description: Python data preparation, SQL analysis and complementary Tableau and Microsoft Power BI dashboards using retail product and transaction data.
LEADERSHIP, AWARDS & RECOGNITION
Faculty commendation for a clear and well-documented capstone presentation, 2024.
Recipient of a university academic progress bursary, 2023.
Organised a peer study group for introductory SQL and Excel coursework at the University of North Trent, 2023-2024.
Volunteered at the university student project showcase, helping prepare presentations and respond to visitor questions, 2024.
CERTIFICATIONS
Power BI Data Analyst (PL-300)
2026
Azure AI Fundamentals (AI-901)
2026
AI Business Professional (AB-730)
2026
Microsoft 365 Copilot & Agent Administration Fundamentals (AB-900)
2026
PROFESSIONAL AFFILIATIONS
Student member, British Computer Society (BCS), engaging with professional computing resources and community activities.
Student member, Association for Computing Machinery (ACM), connecting with computing and data-focused professional resources.
LANGUAGES
English (Native)
Welsh (Beginner)

Matched keywords (14)

  • Wrote and optimised SQL queriesOriginal keyword in JD: SQL query writing and optimisation
  • Python
  • clean and manipulate inconsistent product and transaction datasetsOriginal keyword in JD: Dataset cleaning and manipulation
  • Microsoft Power BI
  • Tableau
  • Excel
  • Dashboard development
  • Azure AI Fundamentals (AI-901)Original keyword in JD: Microsoft Azure AI Fundamentals (AI-901)
  • Power BI Data Analyst (PL-300)
  • AI Business Professional (AB-730)Original keyword in JD: Microsoft AI Business Professional (AB-730)
  • Microsoft 365 Copilot & Agent Administration Fundamentals (AB-900)
  • Completed instructor-led online training and practical exercises in a Microsoft lab environmentOriginal keyword in JD: Live Microsoft lab environments
  • Data analytics
  • portfolio development

Missing keywords (0)

No missing keywords.

Disclaimer: All names, images, contact information, employment histories, achievements, security clearances, military credentials, and other qualifications shown in this content are fictional and used for illustrative purposes only. Any resemblance to actual persons, living or deceased, is purely coincidental.

CV and cover letter examples and recommendations in this article reflect general best practices and professional experience and are provided for guidance only. Results may vary based on role, industry, region, and hiring organisation.

Content Accuracy and Verification: To the fullest extent permitted by applicable law, we do not represent or warrant that CV or cover letter guidance, examples, recommendations, or related information are accurate, complete, current, error-free, or suitable for any particular purpose. You are responsible for independently reviewing and verifying the information before relying on it.

ATS and Cover Letter Scores: ATS scores are estimates calculated using our own formula, informed by publicly available guidance on CV formatting and keyword matching. They are not scores issued or endorsed by an ATS provider or employer. Cover letter scores are also our own assessments. Parsing, weighting, screening criteria, and results vary between systems, employers, job descriptions, and document formats. No score guarantees successful parsing, a particular ranking, an interview, or employment. Use scores as general guidance and independently check your documents against the employer’s requirements. To the fullest extent permitted by applicable law, we accept no liability for errors in scores or parsing, or losses arising from reliance on these assessments. Nothing in this notice excludes liability or limits consumer rights that cannot lawfully be excluded or limited.

Terms and Conditions
 
 
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