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7 Principal Full-Stack Software Engineer - Python, Data and AI CV Templates with Example

By Ria · Last Updated On 19 October 2026
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This guide shows you how to create a Principal Full-Stack Software Engineer - Python, Data and AI CV that presents your technical judgement and delivery clearly. It explains how to organise evidence for this role and choose a format that supports quick review.

A strong CV for this position connects Python services and APIs with trusted data products, secure AI workflows and sound architecture. Use concise bullet points to show your role in delivery, testing, production support and mentoring, with measurable results where you can support them.

Compare the example in 7 layouts using the PNG image previews, then open a preferred version in the editor to customise it. The guide also covers ATS readability, useful CV sections and role-specific terms such as Snowflake, SQL, dbt, Azure Data Factory, data lineage and observability.

What Makes a Great Principal Full-Stack Software Engineer - Python, Data and AI CV in 2026

A strong CV makes architecture decisions, hands-on delivery and technical leadership easy to scan. Clear bullets link services, data pipelines and secure AI workflows to practical outcomes.

Use relevant terms such as Python, API development, Snowflake, SQL, dbt, Azure Data Factory, data quality and testing. Include figures only when you can verify 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

Simple One Column Format places every CV section in one vertical reading path, making career history easy to follow from role to role.

 
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Why This Format Works

  • One reading path lets reviewers follow experience in date order without switching between columns.
  • Section breaks make skills, education and certifications easier to locate within the full career record.
  • Full-width entries give achievement bullets room to retain context and measurable results.

Best For

  • Chronological career histories
  • Detailed achievement descriptions
  • Technical skills lists
  • Several supporting credentials
  • Single-column reading preference

CV Keywords

  • Python
  • backend engineering
  • API development
  • Data engineering
  • data pipelines
  • data modelling
  • reusable data products
  • data quality
  • Data lineage
  • Data reliability
  • Snowflake
  • SQL
  • dbt
  • Azure Data Factory
  • enterprise integration
See all keywords
 

#2 Simple Two Column Format CV Example

Simple Two Column Format divides the CV body into two areas, giving the reader a compact view of related sections.

 
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Why This Format Works

  • Two body columns give experience and supporting sections distinct places within the same CV.
  • Separate reading areas help reviewers locate skills and education without interrupting career history.
  • Compact organisation suits a broad record of roles, credentials and technical detail.

Best For

  • Compact section organisation
  • Parallel content areas
  • Broad technical skills lists
  • Multiple career stages
  • Quick section navigation

#3 Plain One Column with Shaded Labels CV Example

Plain One Column with Shaded Labels uses shaded section labels to help readers find key parts of the CV within a single vertical layout.

 
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Why This Format Works

  • Shaded section labels make experience, education and credentials easier to distinguish at a glance.
  • One-column reading keeps role entries in a clear sequence for career progression.
  • Full-width text gives detailed achievement bullets room to explain outcomes and context.

Best For

  • Clear section identification
  • Sequential career history
  • Detailed achievement descriptions
  • Several distinct CV sections
  • Single-column reading preference
 

#4 One Column Format with Photo Right CV Example

One Column Format with Photo Right places a portrait to the right of the CV body, pairing a visible personal image with a straightforward section flow.

 
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Why This Format Works

  • A single reading path keeps experience entries in sequence for a clear account of career progression.
  • Full-width sections leave room for detailed skills, education and credential information.
  • Consistent vertical order helps reviewers move between roles and supporting sections.

Best For

  • Right-side portrait preference
  • One-column reading preference
  • Ordered career histories
  • Detailed role descriptions
  • Several supporting sections
 

#5 Clean Two Column Sidebar Format CV Example

Clean Two Column Sidebar Format places a sidebar beside the main CV area, keeping supporting information visually apart from the central reading area.

 
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Why This Format Works

  • The sidebar gives supporting CV information a distinct area beside the main role history.
  • Two reading areas help reviewers move between career history and other sections.
  • Separate spaces let skills and credentials sit apart from detailed experience entries.

Best For

  • Sidebar-based section organisation
  • Separate main and supporting areas
  • Detailed skills alongside career history
  • Fast movement between CV sections
  • Multiple supporting credentials
 
 

#6 Two Column Format CV Example

Two Column Format arranges the CV in parallel areas, offering a compact way to view career history alongside other sections.

 
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Why This Format Works

  • Parallel areas give role history and supporting content separate places on the CV page.
  • The two-column reading order makes education and credentials easier to locate alongside experience.
  • Distinct content areas reduce the need to scan one long vertical sequence.

Best For

  • Parallel section organisation
  • Compact CV content
  • Quick access to credentials
  • Long career histories
  • Multiple supporting sections
 

#7 Photo Header Two Column Format CV Example

Photo Header Two Column Format combines a portrait in the header with a two-column CV body, making the image visible while keeping content grouped.

 
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Why This Format Works

  • Two body areas let career history sit apart from other CV sections for easier navigation.
  • Parallel sections help reviewers find education and credentials without losing the role sequence.
  • The header portrait remains visually separate from experience and achievement text.

Best For

  • Portrait in the header
  • Two-column section organisation
  • Parallel content areas
  • Clear career progression
  • Several supporting sections
 
 
 

Why Build Your Principal Full-Stack Software Engineer - Python, Data and AI CV with ResumeInMinutes

ResumeInMinutes helps you organise technical skills, experience 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 experience and technical decisions easy to follow. Keep the focus on reliable software and data delivery, secure automation and measurable results.

 

See Also

 

Frequently Asked Questions

Are these Principal Full-Stack Software Engineer - Python, Data and AI CV templates ATS-friendly?

The examples use clear headings and sections to support scanning. Add relevant terms naturally, such as Python, API development, data pipelines, Snowflake, SQL, dbt, Azure Data Factory and observability.

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 a Principal Full-Stack Software Engineer - Python, Data and AI CV include?

Include a focused summary, relevant experience, technical skills, education and certifications. Show evidence of Python services, APIs, data modelling, secure AI interfaces, testing, production support and technical mentoring.

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 software and data engineering achievements on my CV?

Use concise bullet points to explain the challenge, your contribution and the result. Where you have reliable figures, show the effect on data quality, reliability, delivery time or business operations.

Should I include a photo on my CV?

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

ATS score and CV checks

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ATS Score 96%

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: 28 of 30 (93%) · CV quality: 100%

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

Details
  • ✓Name present: Ewan Johnson
  • ✓Email format: ewan.johnson@example.com
  • ✓Phone format: 07700 90XXXX
  • ✓Experience 1: Job title: Principal Software and Data Engineer
  • ✓Experience 1: Company name: Alderwick Asset Management
  • ✓Experience 1: Work location: London, England
  • ✓Experience 1: Employment dates: Jan 2023 - Present
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: Senior Data Engineer
  • ✓Experience 2: Company name: Cairnbridge Financial Group
  • ✓Experience 2: Work location: London, England
  • ✓Experience 2: Employment dates: Apr 2019 - Dec 2022
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: Data Engineer
  • ✓Experience 3: Company name: Rowan Securities
  • ✓Experience 3: Work location: Edinburgh, Scotland
  • ✓Experience 3: Employment dates: Jan 2017 - Mar 2019
  • ✓Experience 3: Description or achievements: Present
  • ✓Experience 4: Job title: Software Engineer
  • ✓Experience 4: Company name: Westmere Bank
  • ✓Experience 4: Work location: Manchester, England
  • ✓Experience 4: Employment dates: Jul 2013 - Dec 2016
  • ✓Experience 4: Description or achievements: Present
  • ✓Experience 5: Job title: Junior Software Developer
  • ✓Experience 5: Company name: Harbourline Systems
  • ✓Experience 5: Work location: Leeds, England
  • ✓Experience 5: Employment dates: Aug 2011 - Jun 2013
  • ✓Experience 5: 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
Ewan Johnson
07700 90XXXX
ewan.johnson@example.com
ewanjohnson
London, Greater London, EC2A 4BX
SUMMARY
Principal software and data engineer with 15 years of experience delivering platforms for investment and wealth management. Build Python services and APIs alongside dependable data pipelines and reusable data products, helping teams access trusted information for reporting and operations. Bring Snowflake, SQL, dbt and Azure Data Factory expertise to data modelling, quality, lineage and reliability. Develop secure interfaces for AI agent workflows, with controlled access and auditable actions in regulated environments. Set practical architecture and engineering patterns for distributed cloud services, and contribute from technical decisions through testing and production support. Partner across teams and mentor engineers, connecting sound technical judgement with hands on delivery, enterprise integration and clearer observability.
EXPERIENCE
Principal Software and Data Engineer
Jan 2023 - Present
Alderwick Asset Management
London, England
Designed Python backend services and APIs for investment operations, replacing fragmented manual data exchanges with secure, reusable interfaces to portfolio and client platforms.
Built Snowflake data pipelines and dbt models using SQL, with automated data quality tests and documented lineage, giving investment teams reliable, accessible data products for reporting and analysis.
From 2024, developed controlled data interfaces for AI agents and tool-calling applications, applying permission checks and audit records so workflow automation could retrieve approved information and execute defined actions safely.
Set scalable software and data architecture patterns for distributed systems on cloud platforms, strengthening enterprise integration, deployment testing and observability across services.
Mentored engineers through design reviews and implementation support, influencing technical decisions while remaining hands-on through delivery and production support.
Senior Data Engineer
Apr 2019 - Dec 2022
Cairnbridge Financial Group
London, England
Delivered Snowflake-based data products for investment and wealth management teams, translating reporting and operational needs into maintainable data models and SQL transformations.
Introduced dbt modelling and testing practices to pipeline development, improving the traceability of changes and helping analysts identify data quality issues before publication.
Developed Azure Data Factory workflows and Python integrations to bring data from enterprise systems into governed cloud pipelines, with monitoring for failed loads and late-arriving records.
Worked with engineering and business colleagues to document data definitions and lineage, making trusted information easier to discover and reuse across reporting workflows.
Data Engineer
Jan 2017 - Mar 2019
Rowan Securities
Edinburgh, Scotland
Built Python and SQL ingestion processes for market and reference data, creating consistent datasets that supported portfolio reporting and operational checks.
Developed Azure Data Factory pipelines and incremental data models, adding validation and exception reporting to make scheduled processing more reliable.
Produced reusable data sets with documented field definitions and lineage, reducing repeated data preparation for analysts and investment operations staff.
Software Engineer
Jul 2013 - Dec 2016
Westmere Bank
Manchester, England
Developed Python backend components and REST APIs for internal financial services applications, connecting account and transaction workflows with enterprise systems.
Applied code reviews, automated testing and structured release checks to integrations handling sensitive operational data, helping teams identify defects before production deployment.
Investigated service incidents using application logs and monitoring, improving operational documentation and the clarity of recurring issue reports.
Junior Software Developer
Aug 2011 - Jun 2013
Harbourline Systems
Leeds, England
Implemented Python services and SQL queries for financial reporting applications, following established design and testing practices while building early backend engineering experience.
Added data validation and integration checks to scheduled processing jobs, helping maintain accurate outputs for client and operations reports.
LEADERSHIP, AWARDS & RECOGNITION
Recognised by the Alderwick Asset Management engineering group in 2025 for mentoring colleagues and improving shared service design practices.
Coordinated an internal engineering learning forum focused on Python service design, data modelling and production observability.
EDUCATION
BSc Computer Science
2010
University of Sheffield
Sheffield, England
CERTIFICATIONS
Microsoft Certified: Azure Data Engineer Associate
2022
SnowPro Core Certification
2021
TECHNICAL SKILLS
Programming Languages: Python, SQL, Bash
Data Warehouses: Snowflake, PostgreSQL, Microsoft SQL Server
Data Transformation: dbt, Apache Spark, pandas
Cloud Platforms: Microsoft Azure, Amazon Web Services, Google Cloud Platform
Data Integration: Azure Data Factory, Apache Airflow, Fivetran
API Technologies: REST, OpenAPI, FastAPI
Python Frameworks: Flask, Django, pytest
Observability Tools: Azure Monitor, Prometheus, Grafana
Messaging Platforms: Apache Kafka, Azure Service Bus, RabbitMQ
Version Control and Delivery: Git, Docker, Kubernetes
AI and Agent Libraries: LangChain, Semantic Kernel, OpenAI API
SKILLS
Python backend engineering
API development
Data engineering
Data pipelines
Data modelling
Reusable data products
Data quality
Data lineage
Data reliability
Enterprise integration
Distributed systems
Cloud platforms
AI agent workflows
Secure data access
Technical mentoring
PROFESSIONAL AFFILIATIONS
Member, British Computer Society, engaging with a professional community focused on computing practice and software engineering.
Member, DAMA UK, participating in a professional community for data management and data engineering.
LANGUAGES
English (Native)
French (Intermediate)

Matched keywords (28)

  • Python
  • backend engineering
  • API development
  • Data engineering
  • data pipelines
  • data modelling
  • reusable data products
  • data quality
  • Data lineage
  • Data reliability
  • Snowflake
  • SQL
  • dbt
  • Azure Data Factory
  • enterprise integration
  • scalable software and data architecture
  • distributed systems
  • cloud platforms
  • AI agentsOriginal keyword in JD: AI agents OR tool-calling applications OR intelligent workflow automation
  • From 2024, developed controlled data interfaces for AI agents and tool-calling applications, applying permission checks and audit records so workflow automation could retrieve approved information and execute defined actions safely.Original keyword in JD: Secure AI data interfaces
  • testing
  • observability
  • production support
  • Mentored engineers through design reviews and implementation support, influencing technical decisions while remaining hands-on through delivery and production support.Original keyword in JD: Technical leadership
  • Set scalable software and data architecture patterns for distributed systems on cloud platforms, strengthening enterprise integration, deployment testing and observability across services.Original keyword in JD: Engineering patterns and development practices
  • Technical mentoring
  • Set practical architecture and engineering patterns for distributed cloud services, and contribute from technical decisions through testing and production support.Original keyword in JD: Pragmatic delivery
  • Principal software and data engineer with 15 years of experience delivering platforms for investment and wealth management. Build Python services and APIs alongside dependable data pipelines and reusable data products, helping teams access trusted information for reporting and operations. Bring Snowflake, SQL, dbt and Azure Data Factory expertise to data modelling, quality, lineage and reliability. Develop secure interfaces for AI agent workflows, with controlled access and auditable actions in regulated environments.Original keyword in JD: Regulated investment and wealth management

Missing keywords (2)

  • Software development lifecycle
  • Cross-team influence
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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