UK

United Kingdom · Sustainability · Mid-Level (2-5 years)

ESG Data Analyst

Here is the whole job, in plain words. What it is, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

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  • Experience bandMid-Level (2-5 years)
  • Direct reportsNo direct reports
  • Reports toSenior ESG Data Analyst
  • UK framework levelUsually a coordinator, or early in a professional job

Also advertised as Sustainability Data Analyst · ESG Reporting Analyst · Data Analyst (Sustainability)

Built on 43,079 real UK job descriptions we analysed · grounded in what real qualifications cover

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 5 of 7 standards
Qualifi Level 3 Extended Diploma in IT and Data Science Qualifi Ltd · Level 3 · covers 2 of 7 standards
BCS Level 4 Diploma - Digital Modular Programme for Data Analysts BCS, The Chartered Institute for IT · Level 4 · covers 2 of 7 standards
Highfield Level 3 Diploma for Team Leaders and Supervisors (RQF) Highfield Qualifications · Level 3 · covers 2 of 7 standards
Pearson BTEC Level 4 Higher National Certificate in Computing for England Pearson Education Ltd · Level 4 · covers 2 of 7 standards

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Data Analytics Primer NOCN Level 4 certificate for Data Analysts
Data and Descriptive Statistics in Data Science Qualifi Level 3 Extended Diploma in IT and Data Science
Data Analysis BCS Level 4 Diploma - Digital Modular Programme for Data Analysts
Data Analytics Pearson BTEC Level 4 Higher National Certificate in Computing for England
Data visualisation Cambridge OCR Level 3 Alternative Academic Qualification Cambridge Advanced National in IT: Data Analytics (Extended Certificate)

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
AI Governance Fluency using AI responsibly
Sustainability Fluency building care for people and planet into the work
Systems Fluency seeing the whole picture
Your Plan Illustration

Built for ESG Data Analyst

5 units that map to this job, from the qualifications that cover it.

  1. Data Analytics Primer NOCN · covers 5 of 7 standards Level 4
  2. Data and Descriptive Statistics in Data Science Qualifi Ltd · covers 2 of 7 standards Level 3
  3. Data Analysis BCS, The Chartered Institute for IT · covers 2 of 7 standards Level 4
  4. Data Analytics Pearson Education Ltd · covers 2 of 7 standards Level 4
  5. Data visualisation Cambridge OCR · covers 1 of 7 standards Level 3
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as an ESG Data Analyst?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skills this role is being asked for

  • Prompt Engineering & LLM Integration
  • Advanced Data Governance & Quality Frameworks
Why each one is rising

The future skills this job leans on

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

What happens when you start

Next, a free account. We build your ESG Data Analyst plan as you set up, starting with the free AI Fluency module. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

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Keep this read. One email with what AI is changing in this job, the new skills it asks for and where to start free. Nothing else follows unless you confirm your address.

3What this role really is

An ESG data analyst checks and organises the data used in environmental, social and governance reporting.

4What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

Advanced Microsoft Excel (VLOOKUP/XLOOKUP, Pivot Tables, Power Query) Advanced

Cleaning messy supplier data, consolidating utility bills, performing initial data transformations, and creating basic internal reports. Power Query will be your best friend for automating repetitive cleaning tasks.

Python (pandas library) Intermediate

Automating the cleansing and manipulation of larger, more complex datasets that Excel struggles with. You'll be writing scripts to standardise formats and merge information.

SQL (Structured Query Language) Intermediate

Writing queries to extract specific ESG-related data from internal databases like ERPs or HRIS systems when direct reports aren't available.

Power BI / Tableau Intermediate

Updating existing dashboards with new data, creating basic charts for internal presentations, and ensuring data refreshes correctly. You won't be designing complex new dashboards from scratch yet.

MSCI ESG Research, Sustainalytics, EcoVadis (ESG Data Platforms) Intermediate

Pulling reports and benchmarks for specific data points, entering our company's data into these platforms, and understanding how they score us.

Workiva Wdesk / OneTrust ESG (Reporting & GRC) Basic

Entering validated data into predefined templates for our annual sustainability report, following established workflows for data collection.

MS Teams / Slack Intermediate

Communicating with internal data providers, sharing files securely, and collaborating with your team on day-to-day tasks.

5What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choiceComing in Where you are nowThe step above
Data Validation & Error Correction Identifies errors and flags them for supervisor to correct. Follows explicit instructions for validation. Independently identifies and corrects routine data errors within established guidelines. Proposes solutions for novel errors to Senior Analyst. Defines validation rules and processes. Oversees complex error resolution. Mentors others on data quality best practices.
Data Collection Methodology Executes data collection using predefined templates and instructions. No input on methodology. Follows established collection methodologies. Identifies inefficiencies or issues and proposes improvements to Senior Analyst. Designs and refines data collection methodologies. Implements new processes for efficiency and accuracy.
Tool Selection for Analysis Uses tools as instructed (e.g., Excel). Chooses appropriate tools (e.g., advanced Excel, Python, SQL) for specific data cleaning/analysis tasks within existing tech stack. Recommends new tools for specific problems. Evaluates and recommends new data analysis tools or software for team adoption. Defines best practices for tool use.
Stakeholder Communication Responds to direct requests from supervisor. Limited proactive communication. Proactively communicates data requests and deadlines to internal and external data providers. Clarifies data requirements. Escalates non-responsive stakeholders. Manages communication strategy with key data providers. Negotiates data delivery timelines. Presents data insights to cross-functional leads.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

Data Entry Accuracy
The percentage of data points you're responsible for that are correctly entered and match source documentation.
Target · >99.5% accuracy

If you're managing 2,000 data points for energy consumption, we'd expect fewer than 10 errors. Catching that one utility bill entered in MWh instead of kWh before it gets to the report is a win.

On-Time Data Collection
The percentage of required data points collected by internal deadlines from various internal and external sources.
Target · 95% of data points collected on schedule

For the Q2 waste data, you managed to get 98% of the site managers to submit their figures by the 15th of July, allowing enough time for processing.

Query Resolution Time
How quickly you acknowledge and resolve internal data queries or requests for clarification from colleagues or senior analysts.
Target · Acknowledge within 24 hours, resolve within 48 hours (for routine queries)

A Senior Analyst asks for the source of a specific water usage figure; you provide the original invoice and calculation spreadsheet within a day.

Data Completeness for Owned Datasets
Ensuring all required fields and data points for the specific datasets you manage (e.g., water, waste, DE&I) are filled in, or a clear, defensible reason for missing data is documented.
Target · 100% completeness or documented exceptions

You've confirmed that every operational site has submitted its monthly waste tonnage, or for the one missing, you have an email from the site manager explaining the delay and an estimated submission date.

Data Integrity & Audit Readiness
The quality of your data documentation, source tracking, and calculation methodologies, ensuring any number you produce can withstand scrutiny.
  • Auditors find no major non-conformances in your datasets. You can easily trace any reported number back to its original source. Your calculations are clear, logical, and well-documented for anyone to follow. Colleagues can pick up your work and understand it without needing to ask you a hundred questions.
Proactive Problem Solving
Your ability to spot inconsistencies, anomalies, or potential issues in the data before they become bigger problems, and propose solutions.
  • You flag a sudden, unexplained drop in energy consumption and investigate the cause (e.g., a meter error, a new efficiency project). You suggest improvements to data collection templates to prevent future errors. You don't just report a problem
  • you come with ideas on how to fix it.
Effective Data Provider Engagement
How well you build relationships and communicate with internal and external data providers to ensure timely and accurate submissions.
  • Data providers respond to your requests promptly and with the correct information. They don't see your requests as a burden, but understand why the data is needed. You're able to gently nudge people without alienating them, even when chasing the fifth follow-up email.
Contribution to Process Improvement
Your ideas and actions that make our data collection, validation, or reporting processes more efficient and less prone to errors.
  • You suggest a new Power Query step in Excel to automate a repetitive data cleaning task. You document a tricky data collection process that saves future colleagues time. You identify a bottleneck in the current workflow and propose a practical way to smooth it out.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy
Solving Data Puzzles

You get a real kick out of taking messy, disparate data and transforming it into something clean and usable. Finding that one missing piece of information or correcting a subtle error gives you a genuine sense of accomplishment.

Spending an afternoon tracking down why the reported waste tonnage for one site was suspiciously low, only to discover a new contractor was using a different unit of measurement, and then correcting it.

Contributing to Real Impact

You're motivated by the idea that your accurate data helps the company make better decisions about its environmental and social footprint. You want your work to mean something beyond just numbers on a spreadsheet.

Knowing that the energy consumption figures you meticulously verified directly contributed to a decision to invest in solar panels at a major facility.

Building Robust Systems

You enjoy setting up clear processes, creating templates, and improving workflows so that data collection and reporting become more efficient and less prone to errors over time. You like making things better for future you.

Developing a simple Power Query script that automatically cleans and standardises supplier data, saving hours of manual work each quarter.

What frustrates people
  • Inheriting a labyrinth of interconnected Excel files with broken links and no documentation, which somehow holds the entire company's historical ESG data.
  • Constantly chasing down busy operational managers and suppliers who view your data requests as a bureaucratic annoyance and a low priority.
  • The low-grade, persistent fear that an auditor will ask you to justify a number from 18 months ago and you won't be able to find the source email or spreadsheet.
  • Receiving data in wildly inconsistent formats (e.g., PDFs, handwritten notes, different units of measure) and having to standardise it all.
  • Explaining for the third time why a specific data point is needed for a regulatory framework that most people haven't heard of.
What this role does not give you
  • A quiet, predictable, and entirely self-directed work environment – you'll be interacting with lots of people and dealing with unexpected data issues.
  • Immediate, high-level strategic influence – your impact is foundational, ensuring the numbers are right so others can make big decisions.
  • A role where you spend all your time building complex models or doing advanced statistical analysis; a lot of the work is about getting the basics right, meticulously.

8Who you work with

Your work underpins the credibility of our entire sustainability narrative. Accurate data means we can set realistic targets, track progress, and avoid accusations of 'greenwashing'. Get it wrong, and it can seriously damage our reputation and even hit our share price. You're essentially the guardian of our ESG numbers, making sure they stand up to scrutiny.

Inside the business
  • Operations Managers (for energy, waste, water data)
  • HR Team (for diversity, equity, and inclusion metrics)
  • Procurement (for supply chain emissions data)
  • Finance Department (for spend data related to ESG)
  • Sustainability Programme Managers (who need your data for their initiatives)
Outside the business
  • ESG Rating Agencies (e.g., MSCI, Sustainalytics)
  • Third-party Auditors (who check our reported numbers)
  • Data Providers (like utility companies or waste management firms)

9What you need before you start

Not a wish list. The things you would be expected to already have.

  • At least 2 years of experience in a data-focused role, ideally with some exposure to ESG, environmental, or social data.
  • Demonstrable experience with advanced Excel functions (Power Query is a must-have!).
  • Basic proficiency in Python (pandas) or SQL for data manipulation.
  • A foundational understanding of at least one major ESG reporting framework (e.g., GRI, SASB).
  • Proven ability to work meticulously and accurately with large datasets.
  • Strong written and verbal communication skills; you'll be explaining data to non-technical folks.
  • A genuine interest in sustainability and corporate social responsibility – you'll need to care about the 'why'.

10What to practise next

Where the job is going, and what to do about it starting this week.

Advanced Python for Data Engineering

Important within 12-18 months. As our data volumes grow and reporting requirements become more complex, manual data cleaning and simple scripts won't cut it. You'll need to build more scalable and automated data pipelines.

ETL (Extract, Transform, Load) Principles · API Integration for Data Collection · Version Control with Git · Error Handling & Logging · Data Orchestration Tools (e.g., Apache Airflow basics)

  • This quarter: Complete an online course on intermediate Python for data science, focusing on pandas and data manipulation.
  • Next quarter: Start building a more complex Python script that integrates data from two different sources, including error handling.
  • Month 6-9: Learn the basics of Git and start using it for all your Python projects, even small ones.
  • Month 10-12: Research and present on how we could use APIs to automate data collection from one of our key external providers.

Quick win: Refactor one of your existing Python scripts to include basic error handling (e.g., try-except blocks) and add comprehensive comments to explain each step. This makes your code more robust and readable immediately.

11Staying current once you are in

What people here do to keep up
  • Attend webinars or workshops on new ESG reporting regulations (e.g., CSRD updates, ISSB standards).
  • Join relevant professional networks or online communities (e.g., LinkedIn groups for ESG data professionals) to stay informed and share best practices.
  • Take online courses to deepen your Python or SQL skills, especially in areas like data engineering or API integration.
  • Participate in internal 'Lunch & Learn' sessions where colleagues share insights on specific ESG topics or data tools.

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Prompt Engineering & LLM Integration

Critical within 6 months—this is already happening, not future. Competitors are already using Large Language Models (LLMs) like GPT or Claude to draft reports in minutes that used to take hours. Analysts who figure this out will outproduce peers significantly.

  • Context Windows and Token Limits
  • Temperature Settings for Different Tasks
  • RAG (Retrieval Augmented Generation) Architectures
  • Output Validation and Hallucination Detection
  • Prompt Chaining for Complex Analysis

Advanced Data Governance & Quality Frameworks

Important within 12 months. With increasing regulatory scrutiny (like CSRD requiring limited assurance), simply collecting data isn't enough. We need robust systems to ensure data quality, lineage, and auditability from the source.

  • Data Lineage & Audit Trails
  • Data Quality Rules & Checks
  • Data Ownership & Stewardship
  • Metadata Management
  • Data Privacy for ESG (e.g., DE&I data)

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • ESG Reporting Frameworks (GRI, SASB, TCFD, CSRD)
  • GHG Protocol & Carbon Accounting (Scopes 1, 2, 3)
  • Data Validation & Assurance Readiness
  • Materiality Assessment Concepts
  • Supply Chain Data Management Principles

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    ESG Data Assistant (L1)

    1-2 years

    Skills to master

    • Mastering data entry accuracy, understanding basic ESG terminology, effective use of Excel for initial data processing, and consistent documentation.

    You're ready to move on when

    • Consistently delivers accurate data on time with minimal supervision.
    • Demonstrates a clear understanding of our core ESG data collection processes.
    • Proactively identifies minor data discrepancies and flags them for review.
    • Shows initiative in learning new tools or frameworks.
  2. 2

    General Data Assistant / Junior Analyst (non-ESG)

    2-3 years

    Skills to master

    • Strong data cleaning and manipulation skills in Excel and basic Python/SQL, experience with data validation, and an ability to manage multiple data sources. You'd need to quickly pick up ESG-specific frameworks and context.

    You're ready to move on when

    • Proven track record of managing and validating large datasets in previous roles.
    • Demonstrable proficiency in advanced Excel, with some exposure to Python or SQL.
    • A clear interest in sustainability, evidenced by personal projects, courses, or volunteer work.
    • Ability to quickly learn and apply new domain-specific knowledge.
  3. 3

    Recent Graduate (relevant degree with internship)

    0-1 year (to reach L2 equivalent)

    Skills to master

    • Applying academic knowledge to real-world messy data, developing strong practical skills in our tech stack, and understanding the nuances of corporate ESG reporting. An internship with a data or sustainability focus would be a huge advantage.

    You're ready to move on when

    • Graduated with a strong academic record in a quantitative or sustainability-focused degree.
    • Completed an internship where you worked with real-world data and contributed to analysis or reporting.
    • Demonstrates strong problem-solving skills and a proactive attitude to learning.
    • Can clearly articulate the relevance of their studies to ESG data challenges.

15Where this role leads

The long view: This role builds a solid grasp of ESG data and reporting. From here, some move into managing ESG data and reporting, others into senior sustainability strategy roles, or into deep technical data engineering work.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how ESG Data Analyst is actually changing.

Where it leads

Roles this leads to

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from Data Analytics Primer, worked against ESG Data Analyst.

Data Analytics Primer Level 4

Applied to your work in ESG Data Analyst

This unit aims to equip learners with a foundational understanding of data analytics, including its applications and the stages of the data analysis lifecycle. Learners will explore various data types and structures, understand the role of data within an organisation, and recognise the importance of GDPR and compliance requirements in data handling.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 6 Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

See Your Progress Grow Illustration
ESG Data Analyst
  • ESG Reporting Frameworks (GRI, SASB, TCFD, CSRD)
  • GHG Protocol & Carbon Accounting (Scopes 1, 2, 3)
  • Data Validation & Assurance Readiness
  • Materiality Assessment Concepts
  • Supply Chain Data Management Principles
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

ESG Data Analyst is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Senior ESG Data Analyst (L3)

    3-5 years in current role

    You'll move from owning specific datasets to owning entire workstreams, like our Scope 1 & 2 emissions reporting or our full DE&I data programme. You'll also start mentoring junior colleagues and taking the lead on process improvements.

    • Designing and implementing new data collection processes.
    • Automating significant portions of data cleaning and validation using Python.
    • Developing and maintaining complex, interactive dashboards in Power BI/Tableau.
    • Deep expertise in multiple ESG reporting frameworks (e.g., GRI, SASB, TCFD, CSRD).
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be honest, a big chunk of ESG data work can be repetitive and time-consuming. Imagine reclaiming those hours! We're not just talking about theory; we're actively exploring and implementing AI tools to make your daily grind smoother, faster, and frankly, a lot more interesting.

In Sustainability_Corporate_Social, AI isn't here to replace your critical thinking or your meticulous eye for detail. Instead, it's your smart assistant, taking on the heavy lifting of data extraction, summarisation, and initial analysis. This means you can focus more on the 'why' behind the numbers, the strategic insights, and less on the tedious 'how'.

Automated Data Extraction

Use AI tools like Microsoft Syntex or custom scripts to automatically scan and pull out key data points (think kWh, gallons, metric tons) from thousands of unstructured supplier PDFs, utility bills, and invoices. It's like having a super-fast intern who never gets tired.

Competitor Analysis Accelerator

Feed the sustainability reports of our competitors into an AI assistant. It'll quickly summarise their stated goals, key initiatives, and data disclosures for specific topics like water management or human rights, giving you a head start on benchmarking.

Regulatory Summariser

When a new, dense regulation like the CSRD gets updated, use an AI model to summarise the key changes, identify new disclosure requirements, and compare them against our current reporting. No more sifting through hundreds of pages just to find the critical bits.

First-Draft Narrative Generator

Once your data is validated, feed key themes and last year's report into a generative AI. It can create a solid first draft of the narrative for a specific section of our annual sustainability report, saving you hours of staring at a blank page. You'll then refine it with your expertise.

Common questions

Common questions

How do you become an ESG Data Analyst?

Common routes in include ESG Data Assistant (1-2 years), General Data Assistant / Junior Analyst (non-ESG) (2-3 years) and Recent Graduate (relevant degree with internship) (0-1 year (to reach L2 equivalent)). Times vary with prior experience.

Where can an ESG Data Analyst progress to?

This role can lead on to Senior ESG Data Analyst (3-5 years in current role), depending on the skills you build.

What new skills matter most for an ESG Data Analyst?

Increasingly, Prompt Engineering & LLM Integration and Advanced Data Governance & Quality Frameworks. These are the skills our record of this role lists as growing.

Free tools

Want a read on where you stand?

Three free tools, no account needed. Each one ends with a role, a level and a way in.

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17Where to go from here

Other roles at Level 3

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

 More jobs in sustainability

Stay in the field you know and move sideways rather than up.
If you leave this industry

The skills built here, data management, reporting, stakeholder engagement and knowledge of ESG frameworks, apply beyond one sector. They can lead to data analytics roles in other industries, sustainability consulting, or ESG investment analysis.

This role profile is © 2026 Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Start my free AI Fluency module What happens next: a free account, then we build your plan as you set up. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.