UK

United Kingdom · Learning and development · Lead (8-12 years)

Lead Analyst, Learning Analytics

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 bandLead (8-12 years)
  • Direct reports0-2 reports
  • Reports toManager, Learning Analytics & Insights
  • UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Staff Performance Analytics Specialist (L&D) · Learning Analytics Architect · Principal L&D Data Scientist

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

Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 4 of 9 standards
Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 2 of 9 standards
NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 5 of 9 standards
OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 4 of 9 standards
Pearson BTEC Level 4 Higher National Certificate in Computing for England Pearson Education Ltd · Level 4 · covers 3 of 9 standards
Highfield Level 3 Diploma for Team Leaders and Supervisors (RQF) Highfield Qualifications · Level 3 · covers 2 of 9 standards

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

The units you would start with

Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England
Data Analytics Primer NOCN Level 4 certificate for Data Analysts
Data Analysis and Visualisation OTHM Level 7 Diploma in Data Science
Data Analysis Highfield Level 3 Diploma for Team Leaders and Supervisors (RQF)

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
Systems Fluency seeing the whole picture
Your Plan Illustration

Built for Lead Analyst, Learning Analytics

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

  1. Data Analytics Pearson Education Ltd · covers 4 of 9 standards Level 5
  2. Data Analytics Primer NOCN · covers 5 of 9 standards Level 4
  3. Data Analysis and Visualisation OTHM Qualifications · covers 4 of 9 standards Level 7
  4. Data Analysis Highfield Qualifications · covers 2 of 9 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 a Lead Analyst, Learning Analytics?

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 for Analytics
  • Ethical AI & Data Governance in L&D
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 Lead Analyst, Learning Analytics plan as you set up, starting with the free Data 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

This job is about measuring whether learning programmes make a difference to a business. It involves designing the measurement strategy for training initiatives, building data pipelines, and explaining what the results mean. The work sets up how impact gets tracked. It often means guiding junior analysts through complex studies.

4What you'd actually use

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

Power BI (DAX) / Tableau (LODs) Expert

Designing complex, interactive dashboards from scratch, writing advanced DAX/LOD calculations, optimising query performance, and mentoring junior analysts on visualisation best practices. You'll be the go-to person for complex dashboard builds.

SQL / Python (pandas, NumPy, scikit-learn) Expert

Writing complex multi-join queries to extract and merge data from various sources. Using Python for advanced data cleaning, transformation, automation (ETL/ELT scripting), and statistical modelling that Excel just can't handle. You'll build robust, repeatable scripts.

Workday Reporting / Docebo API / SAP SuccessFactors Reporting Advanced

Building custom reports and calculated fields within the HRIS/LMS platforms. Using APIs to pull data programmatically for integration into your analytical pipelines. You'll troubleshoot data discrepancies and understand the underlying data schemas deeply.

Qualtrics / Glint / Perceptyx Advanced

Designing complex surveys with branching logic and advanced question types. Performing statistical analysis (e.g., correlation, text analytics) on results within the platform, and integrating survey data with operational data for deeper insights.

Jira / Asana / Confluence Advanced

Managing complex analytics projects, documenting methodologies, maintaining a comprehensive knowledge base for the team, and tracking progress on multiple initiatives. You'll set up and manage these tools for your projects.

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
Analytical Methodology Choice Proposes options to supervisor for review and approval. Selects methodology for routine problems, consults manager for novel ones. Full authority for technical decisions within project scope; consults Director on strategic alignment.
Data Source Integration Identifies potential data sources, seeks guidance on integration. Independently integrates standard data sources, escalates complex issues. Designs and architects integration strategy for multiple complex data sources; consults Data Engineering on infrastructure.
Project Timeline & Scope Changes Informs supervisor of potential delays, seeks direction. Proposes minor adjustments, informs manager of impact. Makes decisions on scope adjustments within project, consults Director on significant timeline or resource shifts.
Budget Allocation (e.g., for tools/external support) No authority; identifies needs and informs supervisor. Recommends spend up to £1K; requires manager approval. Recommends spend up to £50K for tools or consultants; requires Director approval but your recommendation carries significant weight.

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 Pipeline Reliability
Uptime and accuracy of automated data feeds from HRIS/LMS to analytics platforms.
Target · 99.5% uptime, <0.1% data error rate

Your automated script for pulling LMS completion data runs without issue 29 out of 30 days, and the extracted numbers match source data within 0.05%.

Impact Study Adoption Rate
Percentage of key learning programme recommendations (based on your analysis) that are formally adopted or acted upon by L&D leadership or business stakeholders.
Target · 75% adoption rate for major programme recommendations

After your analysis showed a specific sales training wasn't driving pipeline growth, L&D leadership agreed to overhaul 3 out of your 4 suggested modules, representing a 75% adoption.

Time-to-Insight for Strategic Projects
Average time taken from project initiation (e.g., 'we need to measure X') to delivering actionable insights and recommendations for complex, strategic L&D initiatives.
Target · Average 6-8 weeks for complex studies (down from 10-12)

You delivered a comprehensive ROI analysis for our new leadership development programme in 7 weeks, allowing the L&D Director to present findings to the executive committee ahead of schedule.

Automation Coverage
Percentage of recurring data extraction, cleaning, and basic reporting tasks that are fully automated.
Target · 80% of routine tasks automated

You've built Python scripts and Power Query models that handle 85% of the monthly data preparation for our core L&D dashboards, saving the team roughly 20 hours a week.

Strategic Influence
Your ability to shape L&D strategy and investment decisions through data-driven insights, moving beyond just reporting numbers.
  • You're proactively invited to strategic L&D planning sessions. Your input is sought on new programme design. You're asked to present directly to senior business leaders, not just L&D. Senior stakeholders reference your insights in their own presentations.
Analytical Rigour & Trust
The perceived quality, accuracy, and trustworthiness of your analytical methodologies and findings by both technical and non-technical stakeholders.
  • L&D and business leaders trust your numbers implicitly, even when they're challenging. They come to you for advice on experimental design. You're seen as the 'go-to' person for complex data questions. No major errors are found in your published reports.
Mentorship & Capability Building
Your effectiveness in guiding and developing junior analysts, helping them grow their technical and analytical skills.
  • Junior team members seek your advice and guidance. You run effective code reviews and provide clear, constructive feedback. Your mentees show measurable improvement in their analytical output and autonomy. You're known for 'unsticking' people when they hit a wall.
Data Storytelling Effectiveness
Your ability to translate complex data and statistical findings into clear, compelling, and actionable narratives that resonate with diverse audiences.
  • Your presentations are consistently praised for clarity and impact. Stakeholders immediately grasp the 'so what' of your analysis. You can simplify complex statistical concepts for non-technical audiences without losing accuracy. Your recommendations are clearly understood and acted upon.

7Would you like it

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

What people enjoy
Solving Complex Puzzles

You love diving into messy datasets, untangling conflicting information, and finding the hidden patterns that explain why a learning programme succeeded or failed. It's like being a detective with spreadsheets.

Spending an afternoon trying to reconcile completion data from the LMS with engagement data from a separate content platform, finally figuring out why the numbers don't match up.

Driving Real Business Impact

You get a real buzz from seeing your analysis directly lead to a change in L&D strategy, a redesigned programme, or a tangible improvement in employee performance. You want your work to matter.

Presenting data that convinces the Sales Director to invest in a new coaching programme, and then seeing their team's sales conversion rates improve by 5% six months later.

Building & Automating Systems

You're not content with just doing an analysis once; you want to build robust, automated pipelines and dashboards that make future analysis faster, more reliable, and repeatable. You love making things more efficient.

Spending a week writing Python scripts to automatically pull, clean, and merge monthly HRIS and LMS data, saving the team countless manual hours going forward.

What frustrates people
  • The constant battle with messy, inconsistent data from legacy HRIS and LMS systems.
  • Stakeholders changing the scope or questions halfway through a complex analysis.
  • Having to explain basic statistical concepts repeatedly to non-technical audiences.
  • The political pressure to spin data to support pre-conceived notions about programme effectiveness.
  • Being told 'everyone needs this training now' which makes proper experimental design impossible.
What this role does not give you
  • A perfectly clean, pre-structured dataset to work with every day.
  • A role where all your recommendations are immediately adopted without question.
  • A purely technical role with no need for stakeholder management or 'translation' skills.
  • A predictable, routine workload with no urgent, messy data requests.

8Who you work with

This role directly shapes the efficacy and strategic direction of our entire learning function. Your work ensures that L&D isn't just a cost centre, but a measurable driver of talent development and business performance. You'll be the one providing the evidence that justifies significant investment in our people, or conversely, helping us pivot away from ineffective programmes. Essentially, you're helping us get more bang for our buck when it comes to developing our workforce.

Inside the business
  • L&D Leadership Team (Heads of Learning, Programme Managers)
  • HR Business Partners and HRIS Team
  • Senior Business Leaders (Sales Directors, Operations VPs)
  • Data Engineering and IT Teams
  • Finance Business Partners
Outside the business
  • Learning Technology Vendors (LMS, LRS providers)
  • External Consultants for specialist analytics projects

9What you need before you start

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

  • Proven track record (8+ years) in a dedicated data analysis or business intelligence role, with at least 3-4 years specifically focused on HR, Talent, or Learning & Development analytics.
  • Demonstrable experience designing and implementing end-to-end data pipelines using SQL and Python for data extraction, transformation, and loading (ETL/ELT).
  • Extensive experience building and maintaining complex, interactive dashboards in Power BI or Tableau, including advanced calculations and performance optimisation.
  • Strong understanding of statistical concepts and their practical application in business, including hypothesis testing, regression, and experimental design.
  • Experience mentoring junior analysts or leading small project teams, providing technical guidance and constructive feedback.
  • A portfolio or examples of previous analytical projects that demonstrate your ability to translate complex data into clear, actionable business recommendations.

10What to practise next

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

Advanced Cloud Data Warehousing (e.g., Snowflake, BigQuery)

As our data volumes grow and we integrate more disparate sources, moving towards a dedicated cloud data warehouse is inevitable. You'll need to understand how to design for, query, and optimise data within these environments.

Data warehouse vs. data lake concepts · ELT (Extract, Load, Transform) vs. ETL (Extract, T · Star schema and data normalisation for performance · Cost optimisation strategies in cloud data platfor · Data governance and security within a cloud enviro

  • This quarter: Take an introductory course on Snowflake or BigQuery fundamentals.
  • Next quarter: Participate in discussions with Data Engineering on our future data strategy, offering L&D analytics requirements.
  • Month 6: Build a small proof-of-concept data model for a specific L&D dataset within a cloud sandbox environment.
  • Month 9: Get certified in a relevant cloud data platform (e.g., Snowflake SnowPro Core).

Quick win: Start familiarising yourself with the terminology and core concepts of cloud data warehousing. Read a few articles or watch some YouTube tutorials on Snowflake's architecture. It'll make those conversations with Data Engineering much easier.

Predictive Analytics & Machine Learning Fundamentals

Moving beyond 'what happened' to 'what will happen' is the next frontier. We'll want to predict things like flight risk based on learning engagement, or which employees would benefit most from specific interventions. This requires a basic grasp of ML concepts.

Supervised vs. unsupervised learning · Classification and regression algorithms (e.g., lo · Feature engineering (creating new variables from e · Model evaluation metrics (accuracy, precision, rec · Overfitting and underfitting

  • This quarter: Complete an online course on machine learning fundamentals (e.g., Andrew Ng's Coursera course or a similar Python-based introduction).
  • Next quarter: Experiment with `scikit-learn` in Python to build a simple predictive model on an L&D dataset (e.g., predicting course completion).
  • Month 6: Present a 'Predictive Analytics for L&D' concept to your manager, outlining potential use cases.
  • Month 9: Collaborate with a data scientist on a more complex predictive project, learning from their expertise.

Quick win: Start reading articles on how machine learning is being applied in HR and L&D. Even understanding the vocabulary will give you a significant advantage in future discussions.

11Staying current once you are in

What people here do to keep up
  • Regularly attending industry conferences or webinars on Learning Analytics, People Analytics, or Data Science.
  • Contributing to open-source data projects or maintaining a public GitHub repository of your analytical work.
  • Participating in online data science competitions (e.g., Kaggle) to hone your problem-solving skills.
  • Taking advanced courses in statistics, experimental design, or machine learning.
  • Networking with other Lead Analysts or Data Scientists to share best practices and learn new techniques.

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 for Analytics

Competitors are already using Large Language Models (LLMs) to draft reports, summarise qualitative data, and even generate code in minutes. Analysts who master this will outproduce their peers significantly. This isn't 'future' anymore; it's happening now.

  • Context windows and token limits (how much data an
  • Temperature settings for different tasks (creativi
  • Retrieval Augmented Generation (RAG) architectures
  • Output validation and hallucination detection (kno
  • Prompt chaining for complex, multi-step analysis

Ethical AI & Data Governance in L&D

As we use more AI and collect more granular data on employee performance and learning, the ethical implications and governance requirements become critical. We need to ensure fairness, privacy, and transparency, especially with sensitive people data.

  • Algorithmic bias detection and mitigation strategi
  • Data anonymisation and pseudonymisation techniques
  • Explainable AI (XAI) principles for 'black box' mo
  • Privacy-preserving analytics (e.g., differential p
  • Company policies for AI usage and data handling in

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Kirkpatrick & Phillips Models (Levels 1-5)
  • Advanced Statistical Analysis & Experimental Design
  • Data Modelling & Architecture
  • Performance Consulting (Data-Driven)
  • Learning Analytics & xAPI (Experience API)

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

    Senior L&D Performance Analyst (Internal Promotion)

    Typically 3-5 years as a Senior Analyst

    Skills to master

    • Mastering end-to-end project ownership, developing strong stakeholder influence, and demonstrating initial mentorship capabilities. You'll have built a reputation for reliable, impactful analysis.

    You're ready to move on when

    • Successfully led 5+ complex impact analysis projects independently.
    • Consistently delivered actionable insights that led to programme changes or strategic decisions.
    • Mentored at least two junior analysts on multiple projects, with positive feedback.
    • Proactively identified and proposed new analytical approaches or tools for the team.
  2. 2

    Lead Data Analyst / Data Scientist (from another domain)

    Roughly 8-12 years in a data role, with a strong interest in people analytics.

    Skills to master

    • Transferring advanced analytical and technical skills (SQL, Python, BI tools) to the L&D domain, quickly learning the nuances of learning data and the Kirkpatrick/Phillips models. You'll need to demonstrate a passion for people development.

    You're ready to move on when

    • A strong portfolio of complex analytical projects from previous roles.
    • Demonstrable experience with experimental design and statistical modelling.
    • Clear evidence of strong communication and stakeholder management skills.
    • A genuine interest in learning and development, perhaps through personal projects or prior exposure to HR data.
  3. 3

    Consultant (People Analytics / HR Tech)

    Typically 5-8 years in a consulting role focused on HR or learning analytics.

    Skills to master

    • Adapting consulting experience (client management, project delivery, strategic recommendations) to an in-house context. You'll need to demonstrate a deeper technical 'build' capability beyond just advising.

    You're ready to move on when

    • Managed and delivered multiple people analytics projects for various clients.
    • Experience with different HRIS/LMS platforms and data structures.
    • Strong ability to translate business problems into analytical questions and solutions.
    • Demonstrated ability to build and implement analytical solutions, not just design them.

15Where this role leads

The long view: From here, the path can lead to running the wider measurement function, setting talent analytics strategy across a business, or leading a learning function that puts data at its centre.

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 Lead Analyst, Learning Analytics is actually changing.

Where it leads

Roles this leads to

Also named as a next step, with no page in the catalogue yet:

  • Principal L&D Data Scientist (Individual Contributor)

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, worked against Lead Analyst, Learning Analytics.

Data Analytics Level 5

Applied to your work in Lead Analyst, Learning Analytics

The objective of this unit is to enable learners to understand and apply data analytics techniques for decision-making. Learners will be able to apply descriptive, predictive, and prescriptive analytic methods, utilising statistical methods, to convert raw data into actionable insights and determine the best course of action.

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
Lead Analyst, Learning Analytics
  • Kirkpatrick & Phillips Models (Levels 1-5)
  • Advanced Statistical Analysis & Experimental Design
  • Data Modelling & Architecture
  • Performance Consulting (Data-Driven)
  • Learning Analytics & xAPI (Experience API)
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

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

  1. Manager, Learning Analytics & Insights

    Roughly 3-5 years as a Lead Analyst

    Level 5

    • Vendor Management (strategic partnerships)
    • Enterprise Data Governance (broader scope)
    • Workforce Planning Integration
    • Advanced Predictive Modelling (guiding team efforts)

  2. Principal L&D Data Scientist (Individual Contributor)

    Roughly 3-5 years as a Lead Analyst

    Level 5 (equivalent to Manager)

    • Advanced Machine Learning Engineering (deployment, MLOps)
    • Deep Learning Applications (e.g., NLP for learning content)
    • Real-time Analytics Architectures
    • Advanced Causal Inference Techniques
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a big chunk of your day as an analyst is often spent on the tedious stuff – cleaning data, writing boilerplate code, or drafting summaries. But what if you could offload a significant portion of that to AI? We're not talking about replacing you; we're talking about making you incredibly more productive.

At Zavmo, we're actively exploring and integrating AI tools to make our teams more efficient. For a Lead Analyst in Learning Analytics, this means you'll have access to cutting-edge AI capabilities that can transform how you work, freeing you up for the truly strategic, high-impact challenges.

Automated Data Janitor

Use AI-powered tools (think Python libraries like `pandas-profiling` or dedicated platforms) to automatically detect anomalies, suggest cleaning steps, and harmonise inconsistent data from the LMS and HRIS. It'll give you back hours you usually spend wrestling with messy spreadsheets.

Insight Discovery Engine

Feed thousands of open-ended survey comments or qualitative feedback into an LLM. It'll instantly perform thematic and sentiment analysis, identifying key trends and pulling out direct quotes that would take you days to read and categorise manually. Get to the 'why' faster.

Hypothesis Co-Pilot

Stuck trying to figure out confounding variables for an impact study? Ask an AI assistant to brainstorm 'What other factors besides training could affect sales performance?' or to find external academic research on measuring learning transfer. It's like having a research assistant on demand.

Data Storytelling Assistant

Give an AI model your key charts and data points from an analysis and ask it to generate a first draft of an executive summary or presentation script. It'll help translate complex findings into clear, business-focused language, saving you hours of drafting time.

Common questions

Common questions

How do you become a Lead Analyst, Learning Analytics?

Common routes in include Senior L&D Performance Analyst (Internal Promotion) (Typically 3-5 years as a Senior Analyst), Lead Data Analyst / Data Scientist (from another domain) (Roughly 8-12 years in a data role, with a strong interest in people analytics.) and Consultant (People Analytics / HR Tech) (Typically 5-8 years in a consulting role focused on HR or learning analytics.). Times vary with prior experience.

Where can a Lead Analyst, Learning Analytics progress to?

This role can lead on to Manager, Learning Analytics & Insights (Roughly 3-5 years as a Lead Analyst) and Principal L&D Data Scientist (Individual Contributor) (Roughly 3-5 years as a Lead Analyst), depending on the skills you build.

What new skills matter most for a Lead Analyst, Learning Analytics?

Increasingly, Prompt Engineering & LLM Integration for Analytics and Ethical AI & Data Governance in L&D. 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.

Read the Zavmo manifesto to understand how you will be taught Meet your tutors

Or try another one

17Where to go from here

Other roles at Level 5

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

 More jobs in learning and development

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

The skills involved, advanced analytics, data storytelling, experimental design, and presenting data to senior stakeholders, transfer to other data-heavy fields. Similar analyst or manager roles exist in marketing analytics, product analytics, and operations analytics. Consulting work focused on people data is another option.

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 Data 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.