UK

United Kingdom · Learning and Development · Mid-Level (2-5 years)

Global Learning Analytics Manager

Here is the whole job, in plain words. What it is, a real day, 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.

  • Experience bandMid-Level (2-5 years)
  • Direct reportsNo direct reports
  • Reports toSenior Global Learning Analytics Manager
  • UK framework levelUsually a coordinator, or early in a professional job

Also advertised as Learning Data Analyst · People Analytics Specialist (Learning) · L&D Data Scientist (Junior)

Built on an analysis of 43,079 real UK job descriptions · grounded in qualifications employers recognise

Start with a free Future Fluency check, tuned to Global Learning Analytics Manager

Ten quick questions, one per Future Fluency, asked against this role rather than a generic one. About five minutes, and no card.

Start the check, free

1What this role really is

This role is all about making sense of our learning data. You'll be the person who digs into the numbers from our learning platforms and HR systems to figure out what's actually working (and what isn't). It's a hands-on role where you'll spend most of your time pulling, cleaning, and analysing data, then presenting your findings in a way that makes sense to everyone else. Think of yourself as a detective, but for learning programmes.

2What you'd actually use

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

Power BI / TableauIntermediate

Creating basic charts from clean datasets, applying filters, exporting data, and building simple, interactive dashboards for L&D programme managers. You'll use pre-built dashboards but also create your own from scratch for specific requests.

Cornerstone OnDemand / Workday Learning / Degreed (LMS/LXP)Intermediate

Running standard reports directly from the UI, understanding the basic data schema (users, courses, completions, scores), and extracting raw data for further analysis. You know where to find the numbers.

Workday HCM / SAP SuccessFactors (HRIS)Intermediate

Pulling standard reports on employee demographics, performance ratings, and job roles. You'll often manually join this with learning data in Excel or Power BI to get a richer picture.

Excel (Power Query, XLOOKUP, Pivot Tables)Advanced

Your go-to for data cleaning, transformation, and initial analysis for datasets under, say, 1 million rows. You're a wizard with Power Query for automating repetitive steps and using XLOOKUP for merging data.

SQL (PostgreSQL, T-SQL)Intermediate

Writing basic to intermediate multi-join queries to extract and transform raw data from our data warehouse. You can filter, aggregate, and join tables to get exactly the data you need for your analyses.

Python (pandas, NumPy)Basic

Using Python scripts with pandas for more complex data cleaning, transformation, and basic statistical analysis where Excel just won't cut it. You're comfortable writing and adapting scripts for these tasks.

Qualtrics / SurveyMonkeyIntermediate

Building simple surveys from templates, implementing basic survey logic, and exporting response data for analysis. You know how to get feedback from learners and make sense of it.

Jira / ConfluenceIntermediate

Updating tickets for your analytical requests, viewing project boards to track progress, and reading/contributing to documentation for data models and processes. It's how we keep track of our work.

3What 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 inWhere you are nowThe step above
Data Cleaning MethodologyFollows explicit instructions from supervisor; escalates any deviations.Chooses appropriate cleaning methods for routine data issues; consults manager on complex or novel data quality problems.Designs and implements data cleaning best practices for entire datasets; defines data quality standards for the team.
Dashboard Design & PublicationUpdates existing dashboards with new data under direct supervision; cannot publish new dashboards independently.Designs and publishes simple dashboards for specific business units or programmes, adhering to established design principles; manager reviews before wider release.Designs and deploys complex, interactive dashboards and reports for leadership; establishes dashboard governance and user access protocols.
Analytical Approach for Ad-Hoc RequestsExecutes pre-defined queries and analyses as instructed.Selects appropriate analytical methods (e.g., descriptive stats, basic comparisons) to answer ad-hoc questions; consults manager on more advanced statistical needs.Defines the analytical approach for complex business questions; recommends appropriate statistical models and methodologies.
Stakeholder CommunicationCommunicates findings to immediate supervisor; may draft emails for review.Communicates findings directly to L&D Programme Managers and HRBPs; seeks manager review for executive-level communications.Presents complex findings and recommendations directly to senior business leaders and executives; represents the team in cross-functional meetings.

4How you'll be judged

The scoreboard, honestly: the hard targets, how often each one is actually looked at, and the quiet human signals that never make it onto a dashboard.

Data Refresh Accuracy
How often your scheduled data refreshes and reports are free from errors or inconsistencies.
Target · 99.5% accuracy on all scheduled data refreshes

If you're refreshing the monthly learning engagement dashboard, we'd expect the numbers to match the source systems exactly, every single time. A single mismatch in 200 data points would be a miss.

Ad-hoc Request Fulfilment SLA
The percentage of urgent, one-off data requests you complete within our agreed service level agreement.
Target · 90% of ad-hoc data requests fulfilled within 48 hours

An L&D Programme Manager needs data on course completions for a board report by Friday. If they ask on Wednesday, you'll aim to have it ready by Friday morning, no excuses.

Manual Data Cleaning Time Reduction
The amount of time you save by automating repetitive data cleaning tasks, especially for recurring reports.
Target · Reduce manual data cleaning time for quarterly reports by 10 hours within 12 months

If the Q1 report took 20 hours of manual Excel work, by Q4, you should have automated enough of that process to get it down to 10 hours or less. That's real time back for more interesting analysis.

Dashboard Usage & Adoption
How often the dashboards you build are actually viewed and used by your target audience.
Target · Average 50 unique monthly viewers for your primary dashboards

You build a new dashboard showing leadership programme impact. We'd expect to see at least 50 different people (L&D, HRBPs, business leaders) logging in to check it out each month, not just your manager.

Clarity of Insights
How well you can explain complex data findings to people who aren't data experts, making it easy for them to understand the 'so what?'
  • Stakeholders consistently say your reports are easy to understand. They can repeat your key findings back to you. You're asked to present your findings in team meetings, not just send a report.
Proactive Problem Identification
Your ability to spot potential issues or interesting trends in the data before someone else asks you to look for them.
  • You bring up a potential dip in compliance training completion rates before the Head of L&D notices. You suggest a new way to look at engagement data that no one had considered. You're not just reacting to requests, you're looking ahead.
Data Quality Improvement
Your efforts to identify and help fix underlying data quality issues in our source systems.
  • You've documented 3-5 recurring data errors and worked with IT or the LMS admin to get them resolved. You've implemented new data validation checks in your processes that catch errors early. The overall 'cleanliness' of your source data improves over time.
Informal Mentorship & Support
How you help other team members (especially new joiners or less data-savvy colleagues) understand and use data.
  • New team members come to you with basic data questions. You've shown a colleague how to use a specific Excel function or Power BI filter. You're seen as a helpful resource for data-related queries within the team.

5Would you like it

The honest version. What people enjoy, and what grinds them down.

What people enjoy
Solving Puzzles & Discovering Insights

You love the challenge of taking messy, disparate data and making it tell a coherent story. That moment when a pattern finally emerges from the noise? That's what you live for.

Spending an afternoon wrestling with two different datasets that don't quite match, then finally figuring out how to join them to reveal a new trend in course completion rates.

Making a Tangible Impact

You want your work to actually be used, not just sit in a report. Knowing that your analysis helped a programme manager improve their course or influenced a budget decision is a big win for you.

Presenting data that clearly shows a specific module in a leadership course isn't effective, leading to that module being redesigned and seeing engagement improve in subsequent cohorts.

Continuous Learning & Skill Development

You're always keen to pick up a new technique, a different tool, or a better way to do things. The world of data analytics moves fast, and you enjoy keeping up and expanding your toolkit.

Taking the initiative to learn a new Python library to automate a data cleaning task that used to take hours in Excel, even if it's not explicitly asked of you.

What frustrates people
  • The 'Causality vs. Correlation' Battle: You'll spend a lot of time explaining to senior leaders that just because sales went up after a training programme doesn't automatically mean the training *caused* it. Isolating variables is tough, and you'll be fighting this battle regularly.
  • Garbage In, Garbage Out: Your analysis is only as good as the data you're given. Expect to spend a significant chunk of your time — maybe 60% — cleaning, merging, and validating messy data from the LMS, HRIS, and other systems. They often weren't designed to talk to each other, so it's a constant battle.
  • The Quest for the 'One ROI Number': Leadership will relentlessly ask for a single, simple ROI figure for all L&D spending. You'll explain why this is a methodological nightmare and often a misleading metric, but honestly, they'll probably still ask for it next quarter.
  • Data Silos & Gatekeepers: Getting access to specific data, say from the Finance department for performance metrics or the Sales Ops team for quota attainment, can feel like a full-time job in itself. It involves politics, building relationships, and often, endless meetings.
  • 'Flavour of the Month' Metrics: A new CHRO arrives, and suddenly everything needs to be measured in terms of 'Learning Velocity' or some other buzzword. This can sometimes force you to put your carefully constructed analytics roadmap on hold to chase the latest trend.
  • LMS Reporting Sucks: Let's be real, the built-in reporting modules of most major Learning Management Systems are pretty inflexible and often inadequate. You'll likely find yourself building complex, sometimes brittle, workarounds just to get basic data out.
What this role does not give you
  • A perfectly clean, pre-structured dataset ready for immediate analysis.
  • The ability to make high-level strategic decisions without input or approval.
  • A 'set it and forget it' routine; priorities and data sources will shift.
  • Direct management of a team (at this level).

6Who you work with

This role directly improves the effectiveness and efficiency of our global learning programmes. By providing solid data, you help us identify which training delivers real value, where we need to adjust, and how we can better support our employees' development. Ultimately, your work helps us build a more skilled and capable workforce, which directly impacts our business performance and employee retention.

Inside the business
  • L&D Programme Managers
  • HR Business Partners (HRBPs)
  • Talent Acquisition Team
  • Business Unit Leaders (e.g., Sales, Product)
  • IT & Data Engineering Teams
Outside the business
  • LMS/LXP Vendors (for data queries)
  • Survey Platform Providers (e.g., Qualtrics support)

7What you need before you start

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

  • At least 2 years of hands-on experience in a data analyst role, ideally within HR, Learning & Development, or a related people-focused function.
  • Demonstrable experience with data cleaning, transformation, and manipulation using Excel (Power Query) and SQL.
  • Proven ability to build and maintain dashboards in Power BI or Tableau.
  • A solid understanding of basic statistical concepts (e.g., averages, medians, standard deviation, correlation).
  • Experience working with data from HRIS (e.g., Workday, SuccessFactors) and/or LMS/LXP platforms (e.g., Cornerstone, Degreed).
  • The ability to explain data findings clearly to non-technical audiences, both in writing and verbally.

8What to practise next

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

Advanced SQL & Data Modelling

As our data warehouse grows and our analytical needs become more complex, you'll need to write more efficient and sophisticated SQL queries. This means moving beyond basic joins to things like Common Table Expressions (CTEs) and window functions to build more robust data models.

Common Table Expressions (CTEs) · Window Functions · Basic Data Model Design

  • This quarter: Pick one recurring report you build and try to rewrite the underlying SQL query using CTEs.
  • Next quarter: Find an online course or tutorial specifically on SQL window functions and apply them to a real-world problem.
  • Within 6 months: Proactively suggest improvements to existing SQL queries or data structures to your Senior Manager.

Quick win: Start by refactoring one of your most frequently used SQL queries to make it more readable and efficient. Even small improvements add up.

Basic Statistical Modelling (Regression/Classification)

To move beyond 'what happened' to 'what might happen' or 'why it happened,' you'll need to start applying basic predictive techniques. This means understanding when to use simple regression or classification models to forecast outcomes or identify key drivers.

Linear Regression (Basic) · Logistic Regression (Basic) · Model Evaluation Metrics (R-squared, Accuracy)

  • This quarter: Complete an online course on introductory statistics for data science, focusing on regression.
  • Next quarter: Try to build a very simple linear regression model in Python (using scikit-learn) on a learning dataset, even if it's just for practice.
  • Within 6 months: Propose a small project to your Senior Manager where a basic predictive model could add value, even if it's just a proof of concept.

Quick win: Start by simply understanding the difference between correlation and causation really well. That's the first step to good modelling.

9Staying current once you are in

What people here do to keep up
  • Actively participate in online data analytics communities (e.g., Reddit's r/dataanalysis, LinkedIn groups) to learn from peers and stay updated on trends.
  • Take online courses (Coursera, Udemy, DataCamp) to deepen your skills in Python (pandas), advanced SQL, or introductory statistics.
  • Attend webinars or virtual conferences on people analytics or learning technologies to broaden your understanding of the field.
  • Proactively seek feedback from your manager and senior colleagues on your analytical approaches and data storytelling.

10How the AI economy is changing work like this

Before we ask anything of you, here's what we can already say about AI and work of this kind:

The new skill this role is being asked for: Prompt Engineering & LLM Integration (for Analysts)

Frankly, competitors are already using tools like ChatGPT and Claude to draft reports in 10 minutes that used to take 2 hours. Analysts who figure out how to use these tools effectively will outproduce their peers significantly. It's not just a 'nice to have' anymore; it's becoming essential for efficiency.

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

Your PlanIllustration

Built for Global Learning Analytics Manager

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

  1. Data Analytics PrimerNOCN · covers 6 of 10 standardsLevel 4
  2. Data AnalyticsPearson Education Ltd · covers 5 of 10 standardsLevel 4
  3. Data AnalysisHighfield Qualifications · covers 2 of 10 standardsLevel 3
  4. Data analysis and designPearson Education Ltd · covers 4 of 10 standardsLevel 5
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.

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 (for Analysts)

Frankly, competitors are already using tools like ChatGPT and Claude to draft reports in 10 minutes that used to take 2 hours. Analysts who figure out how to use these tools effectively will outproduce their peers significantly. It's not just a 'nice to have' anymore; it's becoming essential for efficiency.

  • Effective Prompting for Data Summaries
  • Data Validation with LLMs
  • Code Generation & Debugging (SQL/Python)
  • Understanding LLM Limitations ('Hallucinations')

Basic Ethical AI & Data Bias Awareness

As we use more AI in analytics, understanding its ethical implications and potential for bias becomes crucial. Our insights need to be fair and responsible, especially when dealing with people data. You'll need to recognise when an AI model might be perpetuating existing biases in the data.

  • Algorithmic Bias in Data
  • Data Privacy in AI Contexts
  • Transparency & Explainability (Basic)

What you’ll use

Skills this role draws on

Technical

  • Kirkpatrick Model of Evaluation
  • Learning Transfer Analysis (Basic)
  • Data Storytelling & Narrative Design (Developing)
  • Learning Experience Data Governance (Basic)

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

    Junior Data Analyst / Data Associate (any domain)

    1-2 years

    Skills to master

    • Core data cleaning, Excel proficiency (Power Query), basic SQL, understanding of data visualisation tools (Power BI/Tableau).

    You're ready to move on when

    • Consistently delivers accurate data extractions and basic reports.
    • Can independently troubleshoot common data quality issues.
    • Demonstrates curiosity about the business context of the data.
  2. 2

    HR Data Specialist / HRIS Analyst

    2-3 years

    Skills to master

    • Deep understanding of HR data structures (Workday/SuccessFactors), experience with HR reporting, exposure to people-related metrics.

    You're ready to move on when

    • Can pull complex HR reports and explain what the fields mean.
    • Understands the sensitivity and privacy requirements of people data.
    • Has a good grasp of basic HR processes and how they generate data.
  3. 3

    L&D Coordinator with Data Focus

    2-4 years

    Skills to master

    • Familiarity with LMS/LXP platforms, understanding of learning programme lifecycles, strong Excel skills, basic analytical mindset.

    You're ready to move on when

    • Can run standard LMS reports and identify initial trends.
    • Asks 'why' when numbers don't look right in learning data.
    • Shows initiative in automating manual reporting tasks.

11Where this role leads

The long view:Your journey here is really what you make of it. We're committed to providing the opportunities and support for you to grow, whether that's becoming a deep technical expert, a strong people leader, or a strategic business partner. It all starts with making sense of the data.

Pay & demand

Pay and demand for this role will appear here, each figure traced to a named authoritative source (e.g. the ONS Annual Survey of Hours and Earnings, under the Open Government Licence). We don’t show numbers we can’t attribute.

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 Global Learning Analytics Manager is actually changing. In about two minutes, the free confidence check asks where you stand on each of the ten. That's the whole check, and it's what makes the plan yours rather than generic.

Where it leads

Roles this leads to

12The team that's yours

No two people are taught the same way. This is one-to-one, not one-to-many.

Zavmo is a hyper-personalised AI learning platform. Twelve virtual tutors, each with a different way of teaching, and one orchestration agent that picks the right one for the moment. So every single lesson is shaped around you, your role, and the way you learn. Not a course everyone sits through. A conversation built for you, and no one else.

…and nine more, matched to you after your first chat. Meet all twelve

Start free and meet yours

13What it feels like

A conversation, not a course

Because your tutor knows your role, your projects and your last session, learning sounds like this. And it's different for every single person:

Data Analytics PrimerLevel 4

Applied to your work in Global Learning Analytics Manager

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. Remember
  2. Understand
  3. Apply
  4. Analyse
  5. Evaluate
  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.

One to one, not one to many

No two people run this the same way

A course is written once and handed to everyone. This is assembled around you, and keeps changing as it learns you. Five things it reads, and what each one changes.

  1. Your actual work Every lesson is taught against a live piece of your own work, not a worked example from a textbook.
  2. What you already know The first conversation finds your starting point, so you skip what you can already do and spend the time on what you cannot.
  3. The conditions you learn under Not a learning-styles quiz. The evidence does not support those. The dimensions the research does back, read once and used to shape the plan.
  4. How far you got last time It picks up mid-thought. The tutor knows what you said, what you struggled with, and what it asked you to try.
  5. Which tutor suits the moment Twelve of them, each for a different kind of thinking. The one who walks you through a first idea is not the one who stress-tests it.

See how you learn, free. Eight questions, no sign-up. A directional taster; the diagnostic inside Zavmo goes deeper and keeps adapting.

DemonstrateIllustration

Evidenced on your work in Global Learning Analytics Manager

You do not finish by watching something. You finish by showing it on the work you already do, against the measures this job is judged on.

  • Data Refresh AccuracyHow often your scheduled data refreshes and reports are free from errors or inconsistencies.If you're refreshing the monthly learning engagement dashboard, we'd expect the numbers to match the source systems exactly, every single time. A single mismatch in 200 data points would be a miss.99.5% accuracy on all scheduled data refreshes
  • Ad-hoc Request Fulfilment SLAThe percentage of urgent, one-off data requests you complete within our agreed service level agreement.An L&D Programme Manager needs data on course completions for a board report by Friday. If they ask on Wednesday, you'll aim to have it ready by Friday morning, no excuses.90% of ad-hoc data requests fulfilled within 48 hours
  • Manual Data Cleaning Time ReductionThe amount of time you save by automating repetitive data cleaning tasks, especially for recurring reports.If the Q1 report took 20 hours of manual Excel work, by Q4, you should have automated enough of that process to get it down to 10 hours or less. That's real time back for more interesting analysis.Reduce manual data cleaning time for quarterly reports by 10 hours within 12 months
  • Dashboard Usage & AdoptionHow often the dashboards you build are actually viewed and used by your target audience.You build a new dashboard showing leadership programme impact. We'd expect to see at least 50 different people (L&D, HRBPs, business leaders) logging in to check it out each month, not just your manager.Average 50 unique monthly viewers for your primary dashboards
These are this job's own measures, with its own targets. Nothing is marked evidenced, because nobody has started this yet. Yours would fill in from the work you bring.

Your passport

This isn't a certificate you file away. It's a passport to the life you're designing.

Every credit you earn and every fluency you build adds up: evidence where it counts, carried with you. Zavmo keeps the map: where you are, where you're heading, and the next step, at your pace, around your life. From Global Learning Analytics Manager to Senior Global Learning Analytics Manager (L3), and whatever you decide comes after.

Level 3 · in progressAI Fluency→ Senior Global Learning Analytics Manager (L3)→ your design
Where this takes you

Your journey here is really what you make of it. We're committed to providing the opportunities and support for you to grow, whether that's becoming a deep technical expert, a strong people leader, or a strategic business partner. It all starts with making sense of the data.

See Your Progress GrowIllustration
Global Learning Analytics Manager
  • Kirkpatrick Model of Evaluation
  • Learning Transfer Analysis (Basic)
  • Data Storytelling & Narrative Design (Developing)
  • Learning Experience Data Governance (Basic)
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.

14The 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

Global Learning Analytics Manager is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Senior Global Learning Analytics Manager (L3)

    3-5 years in current role

    You'll move from owning specific analytical tasks to leading entire analytical projects from start to finish. You'll also start mentoring junior analysts and taking on more complex, predictive analysis.

    • Predictive Modelling (Intermediate): Applying regression and classification models to forecast outcomes, like identifying attrition risk or predicting training effectiveness.
    • Experimentation Design (A/B Testing): Designing and analysing A/B tests for learning interventions to measure their causal impact.
    • Advanced Data Storytelling: Crafting compelling narratives for executive audiences, focusing on strategic recommendations and business impact.
    • Data Model Design (Intermediate): Contributing to the design of more robust data models for the learning analytics function.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a lot of data work can be repetitive and time-consuming. But what if you could offload some of that grunt work to AI? We're not talking about replacing you, but giving you superpowers. Our AI Productivity Hub is designed to help you reclaim hours every week, so you can focus on the really interesting stuff: finding those 'aha!' insights.

For a Global Learning Analytics Manager, AI isn't just a buzzword; it's a practical tool that can seriously speed up your data cleaning, analysis, and reporting. Imagine spending less time wrestling with spreadsheets and more time crafting compelling stories from the data. That's the goal here.

Automated Data Harmonisation

Use AI scripts to automatically clean, map, and merge disparate datasets from our LMS (Cornerstone), HRIS (Workday), and engagement surveys (Qualtrics). The AI flags anomalies for your human review, cutting down on hours of manual reconciliation. Think of it as your super-efficient data assistant.

Thematic Feedback Analysis

Got thousands of open-ended comments from course feedback surveys (our 'smile sheets')? Feed them into an LLM to instantly identify and quantify key themes, sentiment, and actionable suggestions. This replaces hours of tedious manual reading and categorisation, giving you insights in minutes.

Pre-Meeting Research Synthesis

Need to quickly get up to speed on a new learning theory or analytical method? Ask an AI assistant to 'Summarise the top 5 academic papers from the last 3 years on measuring learning transfer for soft skills.' You'll get instant domain expertise, helping you prepare for high-stakes meetings with business leaders much faster.

Executive Summary Drafting

After you've built a complex dashboard in Tableau, you can paste screenshots or key data points into an LLM. Prompt it to 'Write a 3-bullet point executive summary for a CHRO explaining the key insights and recommended actions from this data.' This saves you valuable time crafting that perfect, punchy summary.

Common questions

Common questions

How do you become a Global Learning Analytics Manager?

Common routes in include Junior Data Analyst / Data Associate (any domain) (1-2 years), HR Data Specialist / HRIS Analyst (2-3 years) and L&D Coordinator with Data Focus (2-4 years). Times vary with prior experience.

Where can a Global Learning Analytics Manager progress to?

This role can lead on to Senior Global Learning Analytics Manager (L3) (3-5 years in current role), depending on the skills you build.

What level is a Global Learning Analytics Manager in the UK?

This role aligns to RQF Level 3 on the UK framework, a guide to the depth of qualification it maps to, not a hard entry bar.

What new skills matter most for a Global Learning Analytics Manager?

Increasingly, Prompt Engineering & LLM Integration (for Analysts) and Basic Ethical AI & Data Bias Awareness. These are the areas where the higher-paid, future-proof work is heading.

The honest bit

You’ve started things before

Most of them were built for a room full of people who aren’t you. A cohort moves on whether or not your week allowed it, and by the third week the thing you’re behind on becomes the reason you stop opening it.

There’s no cohort here, and no timetable to fall behind. Before anything starts, Zavmo asks when you’re sharpest and how long you can realistically sit down for, then builds the sessions around those answers. A bad fortnight changes your pace. It doesn’t put you behind.

And you only pay once you start learning. Searching and planning are free, and you can cancel any time — so the cost of finding out is an afternoon, not a year.

What it costs

Less than one coaching session. Every month.

A single career-coaching hour costs more than a month of this, and it ends when the hour does. Zavmo doesn't. It's £70 a month, about £2.30 a day, for a companion that knows a Global Learning Analytics Manager, works on the job you actually do, and keeps going at your pace rather than a timetable's.

  • Searching and planning stay free. You only pay when you start learning.
  • Your credits are yours. Regulated, and they don't vanish when a subscription ends.
  • Cancel any time and billing stops. No notice period, no minimum term.

Your path, personalised

You have the map. Walking it is the part we do together.

This route runs to 10 national skill standards. That is a real journey.

Zavmo shapes a learning experience as unique as you are. It fits how you learn, your pace and the work you already do. Every step stays benchmarked to recognised national standards. That’s the plan for becoming a Global Learning Analytics Manager: personal to you, and it still counts. The first steps are free.

Independent research finds well-designed intelligent tutoring performs nearly as well as one-to-one human tutoring: VanLehn (2011), Educational Psychologist.

A private tutor in the UK averages £35–40 an hour . Zavmo is £70/month.

A real plan on learn.zavmo.ai: Ofqual-regulated units, credits, and a three-month run at your own pace.
Start free No commitment. See your first steps free.

15Where 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.

Other roles in Learning and Development

Stay in the field you know and move sideways rather than up.

If you leave this industry

The skills you'll build in this role are highly transferable. You could move into broader People Analytics roles, Data Science positions in other business functions (e.g., Marketing Analytics, Product Analytics), or even specialise in HR Technology and Data Architecture. The demand for strong data professionals who can tell a story with numbers is only growing.

Not sure this is the right direction?

Work out what you actually want from work first, then come back and see which roles fit it. Takes about ten minutes.

This role profile is © 2026Growth 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. Build my plan, free Here's the first ten minutes: a 2-minute confidence check → your personalised roadmap → meet the tutors matched to you. No card, cancel any time. 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.