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7 Head of Data & Analytics Retail CV Templates with Example

By Ria · Last Updated On 17 October 2026
CV example — page 1

This guide shows you how to create a Head of Data & Analytics Retail CV that presents your leadership and data experience clearly. You will learn how to organise relevant evidence and choose a layout that is easy to review.

A strong CV for this role links data strategy and platform delivery to retail priorities such as trading, stock, customer and finance reporting. Clear evidence can show how you lead teams, improve governance and help commercial colleagues use trusted insights.

Explore the example presented in 7 layouts, compare their PNG image previews, and open a preferred example in the editor to customise it. The guide also covers ATS readability, useful CV sections and role-specific terms such as Azure Synapse, Microsoft Fabric, Azure Data Factory, Power BI, data stewardship and self-service reporting.

What Makes a Great Head of Data & Analytics Retail CV in 2026

A strong CV makes leadership, platform change and commercial impact easy to find. Show how your work supports dependable reporting and better retail decisions.

Use concise bullets with relevant terms such as Azure Data Lake, Synapse, Power BI, data governance and team development. Add figures only when you can support them.

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

A single-column page keeps the career history in one reading path, a practical choice when you want each role to follow the last.

 
CV example — page 1   CV example — page 2

Why This Format Works

  • Career history follows one clear reading path, making role progression easy to trace.
  • Education and credentials sit within the same sequence as experience, keeping the full record together.
  • Skills can be reviewed alongside detailed work history without switching between parallel columns.

Best For

  • Detailed role-by-role experience
  • A linear review of career progression
  • CVs with several supporting sections
  • Readers who prefer one reading path
  • Skills and credentials alongside experience

CV Keywords

  • Data strategy
  • data platform roadmap
  • Data engineering leadership
  • Analytics team leadership
  • Azure Synapse
  • Power BI
  • on-premises data warehouse migration
  • Azure Data Lake
  • Microsoft Fabric
  • Azure Data Factory
  • Databricks
  • Logic Apps
  • SSIS
  • SSRS
  • Data governance
See all keywords
 

#2 Simple Two Column Format CV Example

Two columns separate parts of the CV while keeping its sections visible together, a useful choice for readers who want a structured overview.

 
CV example — page 1   CV example — page 2

Why This Format Works

  • Separate content areas help reviewers distinguish career history from supporting information.
  • The two-column structure brings several CV sections into view together.
  • Role details remain easier to follow when supporting sections have their own space.

Best For

  • A structured overview of experience
  • CVs with varied supporting sections
  • Readers comparing career details and credentials
  • Skills and education beside work history
  • Applications needing a clear section hierarchy

#3 Plain One Column with Shaded Labels CV Example

Shaded section labels mark the CV's main parts within a single-column layout, helping readers find each section as they follow the career history.

 
CV example — page 1   CV example — page 2

Why This Format Works

  • Shaded labels make experience, skills and education easier to locate on the page.
  • A single-column reading order keeps role history straightforward to follow.
  • Clear section markers help reviewers move between credentials and work experience.

Best For

  • CVs with clearly named sections
  • A straightforward career-history sequence
  • Readers who use section labels to navigate
  • Experience supported by education and credentials
  • A restrained, readable page
 

#4 One Column Format with Photo Right CV Example

A portrait sits to the right of the single-column CV, adding a clear visual point of reference while keeping the reading path intact.

 
CV example — page 1   CV example — page 2

Why This Format Works

  • The single-column sequence lets reviewers follow experience from one role to the next.
  • Separate section headings help readers find education and credentials in the CV.
  • The portrait adds a personal visual element without dividing the written content into columns.

Best For

  • A CV with a portrait
  • One continuous reading path
  • Role-by-role career detail
  • Readers who value a visual reference
  • Experience with supporting sections
 

#5 Clean Two Column Sidebar Format CV Example

A two-column layout with a sidebar gives supporting details a distinct area beside the main CV content, helping readers compare sections at a glance.

 
CV example — page 1   CV example — page 2

Why This Format Works

  • The sidebar separates supporting details from the main career history.
  • Reviewers can scan distinct CV areas without losing the overall reading order.
  • A dedicated area gives skills and credentials a clear place in the layout.

Best For

  • A CV with distinct supporting details
  • Skills beside detailed work history
  • Readers comparing several CV sections
  • Career history paired with credentials
  • A clear sidebar structure
 
 

#6 Two Column Format CV Example

Two columns give this CV a clearly divided structure, a practical option when readers need to review experience and supporting sections side by side.

 
CV example — page 1   CV example — page 2

Why This Format Works

  • Separate columns help reviewers distinguish career details from other CV sections.
  • The layout presents several areas together for a broad view of the background.
  • A clear division between content areas supports quick movement through the document.

Best For

  • Experience with several supporting sections
  • A side-by-side view of CV content
  • Readers reviewing career details and credentials
  • Skills and education alongside work history
  • A clearly divided document
 

#7 Photo Header Two Column Format CV Example

A photo-led header introduces the two-column CV, giving the page a visible personal element above its organised sections.

 
CV example — page 1   CV example — page 2

Why This Format Works

  • The two-column arrangement gives career history and supporting sections separate visual areas.
  • A photo in the header adds a clear point of visual identification.
  • The page structure helps reviewers move between experience, skills and education.

Best For

  • A CV with a photo-led header
  • Career history with separate supporting sections
  • Readers who prefer distinct content areas
  • A visual introduction above CV details
  • Experience, skills and education together
 
 
 

Why Build Your Head of Data & Analytics Retail CV with ResumeInMinutes

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

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

Choose a format that makes your platform roadmap, team leadership and delivery record easy to follow. Keep the focus on trusted data services and the retail outcomes they support.

 

See Also

 

Frequently Asked Questions

Are these Head of Data & Analytics Retail CV templates ATS-friendly?

The examples use clear headings and sections to support scanning. Include relevant terms naturally, such as Azure Synapse, Microsoft Fabric, SQL data warehousing, Power BI, data governance and Azure Data Factory.

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 an example in the editor to customise it.

What should a Head of Data & Analytics Retail CV include?

Include a focused summary, relevant experience, skills, education and certifications. Show evidence of data strategy, platform migration, team leadership, data quality, governance and commercial reporting.

Which CV layout works well for this role?

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

How can I show data leadership achievements on my CV?

Use concise bullet points to explain the work, your contribution and the result. Add supported measures for outcomes such as service improvement, reporting quality, delivery progress or adoption of self-service insight.

Should I include a photo on my CV?

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

ATS Score 91%

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 = 100 × (confirmed matches + 0.8 × possible matches) ÷ total keywords, rounded and capped at 100. Possible matches still need checking. Saved generation audits use their saved keyword percentage.
  • 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%

Keywords: 26 matched + 13 possible of 41 (89%) · CV quality: 90%

CV quality 90% · Weight 20% · Issues (1)

Details
  • ✓Name present: Eleanor Hall
  • ✓Email format: eleanor.hall@example.com
  • ✗Phone format: 07700 90XXXX
  • ✓Experience 1: Job title: Head of Data & Analytics
  • ✓Experience 1: Company name: Alder & Finch Retail
  • ✓Experience 1: Work location: Bristol, United Kingdom
  • ✓Experience 1: Employment dates: Aug 2020 - Present
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: Data Platform and Analytics Manager
  • ✓Experience 2: Company name: Meridian Outdoor Group
  • ✓Experience 2: Work location: Nottingham, United Kingdom
  • ✓Experience 2: Employment dates: Jan 2017 - Jul 2020
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: Senior Data Engineer
  • ✓Experience 3: Company name: Vale & Rowe Department Stores
  • ✓Experience 3: Work location: Leicester, United Kingdom
  • ✓Experience 3: Employment dates: Jun 2013 - Dec 2016
  • ✓Experience 3: Description or achievements: Present
  • ✓Experience 4: Job title: Data Warehouse Developer
  • ✓Experience 4: Company name: Cedar Lane Retail Group
  • ✓Experience 4: Work location: Derby, United Kingdom
  • ✓Experience 4: Employment dates: Sep 2010 - May 2013
  • ✓Experience 4: Description or achievements: Present
  • ✓Experience 5: Job title: Junior Reporting Analyst
  • ✓Experience 5: Company name: Northfield Consumer Goods
  • ✓Experience 5: Work location: Sheffield, United Kingdom
  • ✓Experience 5: Employment dates: Jul 2008 - Aug 2010
  • ✓Experience 5: Description or achievements: Present
  • ✓No placeholder fields
  • ✓No repeated achievement bullets
  • ✓No empty bullets

Header: Check the phone number: “07700 90XXXX”. Use 7–15 digits, with an optional country code.

Expand details to review the checks.

Text Based CV 100% · Weight 20%

Parsed text
Eleanor Hall
07700 90XXXX
eleanor.hall@example.com
eleanorhall
Bristol, Bristol, BS1 4ST
SUMMARY
Data and analytics leader with 18 years of experience across omnichannel retail and consumer businesses, connecting data platforms and reporting to trading, stock, customer and finance priorities. Lead engineering and analytics teams through cloud platform modernisation, migration and governance, using Azure services and SQL warehousing to provide dependable reporting and trusted insight. Partner with commercial, technology and executive stakeholders to shape delivery plans, develop teams and expand self-service Power BI. Bring a computer science degree and Microsoft credentials, with experience spanning legacy SSIS and SSRS environments through recent Fabric and Databricks adoption.
EXPERIENCE
Head of Data & Analytics
Aug 2020 - Present
Alder & Finch Retail
Bristol, United Kingdom
Set the data platform roadmap for a multi-channel retailer, connecting engineering and analytics priorities to trading, stock availability, customer and finance needs across UK and partner-led international operations.
Led data engineers and analysts through delivery planning and career development, introducing clearer service ownership and review practices that improved coordination between technology and commercial teams.
Directed the move of priority warehouse workloads to Azure Data Lake and Synapse using Azure Data Factory, retaining controlled SQL reporting while retiring duplicated legacy feeds in manageable stages.
Since 2024, sponsored Microsoft Fabric and Databricks evaluations for selected analytics workloads, comparing governance, integration and operating needs before agreeing limited production adoption.
Established data-owner and steward responsibilities, quality checks and catalogue standards, giving business teams clearer definitions and traceable sources for key performance measures.
Improved Power BI model design and release controls, enabling commercial teams to build self-service views while maintaining agreed definitions for executive and operational reporting.
Directed an on-premises data warehouse migration plan covering SSIS dependencies and SSRS report replacement, sequencing decommissioning alongside Azure delivery to maintain operational reporting.
Introduced Power BI data literacy sessions with commercial teams, using sales and stock examples to help colleagues interpret trusted measures and build self-service reports.
Data Platform and Analytics Manager
Jan 2017 - Jul 2020
Meridian Outdoor Group
Nottingham, United Kingdom
Managed a combined data engineering and reporting team supporting online, store and wholesale functions, balancing daily service stability with a phased cloud migration.
Delivered Azure Data Factory pipelines into Azure Data Lake and introduced Azure Synapse workloads as the service became available, reducing reliance on tightly coupled warehouse integrations.
Reworked Power BI semantic models for sales, returns and stock reporting, helping buyers and regional managers investigate trading changes without repeated bespoke extracts.
Introduced delivery backlogs, data-quality checks and stakeholder sign-off for priority reporting products, improving release predictability and clarifying accountability for shared metrics.
Connected supplier and ecommerce feeds through Azure Logic Apps and Azure Data Factory, using failure alerts and retry handling to make priority retail data integrations easier to monitor and recover.
Translated buyer and operations requirements into an agile reporting backlog, prioritising KPI changes and coordinating iterative releases with engineers and business stakeholders.
Senior Data Engineer
Jun 2013 - Dec 2016
Vale & Rowe Department Stores
Leicester, United Kingdom
Developed SQL Server warehouse structures and SSIS integrations for point-of-sale, ecommerce and merchandising data, creating consistent product and sales views for analysts.
Replaced fragmented SSRS reports with governed reporting datasets and, from 2015, supported initial Power BI dashboards for trading teams to explore daily performance.
Worked with finance and trading stakeholders to reconcile sales and returns definitions, documenting business rules that reduced recurring discrepancies in management reports.
Analysed reporting requirements with finance and trading teams, mapping source fields to shared sales and returns models so recurring KPI discrepancies could be traced to their origins.
Data Warehouse Developer
Sep 2010 - May 2013
Cedar Lane Retail Group
Derby, United Kingdom
Built and maintained SQL Server and SSIS loads for store sales, stock movements and supplier records, with validation routines that exposed incomplete feeds before reporting deadlines.
Produced SSRS reports for finance and operations, turning recurring spreadsheet requests into scheduled views with documented calculations and consistent filters.
Investigated data-load failures with source-system teams and recorded recovery procedures, improving continuity for weekly trading and stock reporting.
Junior Reporting Analyst
Jul 2008 - Aug 2010
Northfield Consumer Goods
Sheffield, United Kingdom
Prepared SQL extracts and checked source data for sales and inventory reports, resolving record mismatches with operational colleagues before month-end review.
Updated SSRS report layouts and maintained reference documentation, making routine reporting easier for finance and supply teams to interpret.
PORTFOLIO
Title: Retail cloud data platform migration
Link:
Description: Work sample: staged transition of priority warehouse workloads to Azure Data Lake and Synapse, with Azure Data Factory pipelines and controlled SQL reporting.
Title: Retail self-service reporting and governance
Link:
Description: Work sample: Power BI model and release practices paired with data ownership, quality checks and catalogue standards for trusted commercial measures.
LEADERSHIP & AWARDS
Led data engineering and analytics team development at Alder & Finch Retail, adding delivery planning and career progression to service ownership practices.
Introduced shared data quality checks and catalogue standards at Alder & Finch Retail, strengthening ownership of key commercial measures.
EDUCATION
Bachelor of Science in Computer Science
2006
University of Leicester
Leicester, United Kingdom
CERTIFICATIONS
Microsoft Certified: Azure Data Engineer Associate
2020
Microsoft Certified: Power BI Data Analyst Associate (PL-300)
2022
Microsoft Fabric Analytics Engineer Associate
2024
TECHNICAL SKILLS
Data Warehousing: SQL Server, Azure Synapse, Azure SQL Database
Data Integration: Azure Data Factory, SSIS, Azure Logic Apps
Cloud Data Storage: Azure Data Lake Storage Gen2, Azure Blob Storage, Amazon S3
Business Intelligence: Power BI, SSRS, Microsoft Excel
Data Modelling: Power BI Desktop, SQL Server Management Studio, Azure Data Studio
Analytics Platforms: Microsoft Fabric, Databricks, Azure Analysis Services
Programming and Query: T-SQL, SQL, DAX
Data Formats: Parquet, JSON, CSV
Data Orchestration: Apache Airflow, Azure Data Factory Pipelines, Databricks Workflows
Development and Version Control: Git, Azure DevOps, Visual Studio
SKILLS
Data strategy
Platform roadmaps
Data engineering leadership
Analytics team leadership
Data governance
Data quality assurance
Data stewardship
Data modelling
Metadata cataloguing
Self-service BI
Data literacy
Agile delivery
Commercial judgement
Business KPI analysis
Stakeholder management
PROFESSIONAL AFFILIATIONS
Member, DAMA UK, contributing to a professional community focused on data management and governance.
Member, BCS, The Chartered Institute for IT, participating in its professional community for computing practitioners.

Matched keywords (26)

  • Data strategy
  • data platform roadmap
  • Data engineering leadership
  • Analytics team leadership
  • Azure Synapse
  • Power BI
  • on-premises data warehouse migration
  • Azure Data Lake
  • Microsoft Fabric
  • Azure Data Factory
  • DatabricksOriginal keyword in JD: Databricks OR dbt OR Synapse training
  • Logic Apps
  • SSIS
  • SSRS
  • Data governance
  • Established data-owner and steward responsibilities, quality checks and catalogue standards, giving business teams clearer definitions and traceable sources for key performance measures.Original keyword in JD: Data ownership and stewardship
  • Data Modelling
  • data literacy
  • Agile delivery
  • Analysed reporting requirements with finance and trading teams, mapping source fields to shared sales and returns models so recurring KPI discrepancies could be traced to their origins.Original keyword in JD: Business requirements analysis
  • Led data engineers and analysts through delivery planning and career development, introducing clearer service ownership and review practices that improved coordination between technology and commercial teams.Original keyword in JD: Team development and career progression
  • Commercial judgement
  • omnichannel retail
  • computer scienceOriginal keyword in JD: Data science OR computer science OR information systems OR mathematics OR statistics OR related discipline, or equivalent experience
  • Microsoft Certified: Azure Data Engineer Associate
  • Databricks

Missing keywords (2)

  • Business KPIs
  • Problem-solving

Possible matches (13)

  • Azure enterprise data platform80% credit: 1.95 percentage points of 2.44 possible.
  • SQL data warehousing80% credit: 1.95 percentage points of 2.44 possible.
  • Cloud data architecture80% credit: 1.95 percentage points of 2.44 possible.
  • Data platform modernisation80% credit: 1.95 percentage points of 2.44 possible.
  • Data pipelines and integrations80% credit: 1.95 percentage points of 2.44 possible.
  • Data quality and assurance80% credit: 1.95 percentage points of 2.44 possible.
  • Metadata and data catalogues80% credit: 1.95 percentage points of 2.44 possible.
  • Self-service business intelligence80% credit: 1.95 percentage points of 2.44 possible.
  • Commercial stakeholder management80% credit: 1.95 percentage points of 2.44 possible.
  • Executive stakeholder management80% credit: 1.95 percentage points of 2.44 possible.
  • Delivery planning and prioritisation80% credit: 1.95 percentage points of 2.44 possible.
  • Commercial data services80% credit: 1.95 percentage points of 2.44 possible.
  • Power BI Data Analyst (PL-300)80% credit: 1.95 percentage points of 2.44 possible.
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.

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