UK

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

Performance Analytics Assistant

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 Performance Analytics Assistant
  • UK framework levelUsually a coordinator, or early in a professional job

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

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 Performance Analytics Assistant

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 one digging into numbers from our learning platforms and HR systems, helping us figure out if our training programmes are actually working. It's not just about pulling reports; it's about spotting trends, understanding what they mean, and helping the team make smarter decisions about how we develop our people. You'll be a key part of showing the business that L&D isn't just a cost centre, but a real driver of performance.

2What you'd actually use

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

Workday HCM / SAP SuccessFactorsIntermediate

Pulling standard and custom reports on employee demographics, tenure, performance ratings, and learning assignments. You'll need to navigate the UI confidently and understand how to extract the data you need.

Cornerstone OnDemand / DegreedIntermediate

Running pre-built and custom reports on course completions, enrollments, and 'seat time'. You'll be manually exporting CSVs for analysis ('Scraping the LMS') and starting to use more advanced reporting features.

Tableau Desktop / Power BI DesktopIntermediate

Connecting to Excel/CSV files and basic SQL queries. Building simple bar/line charts and dashboards from established templates, and beginning to design new interactive dashboards for business users. You'll be writing basic calculations.

Microsoft ExcelAdvanced

You'll be living in Excel. Proficient in PivotTables, VLOOKUP/INDEX-MATCH, complex formulas for cleaning and summarizing manually exported data. You'll be starting to use Power Query (M language) for automating data cleaning pipelines.

SQL (PostgreSQL / T-SQL)Intermediate

Writing basic `SELECT`, `FROM`, `WHERE` clauses to query clean, well-documented tables. You'll be starting to write more complex multi-table `JOIN`s and `GROUP BY`s to pull specific datasets for analysis.

Qualtrics / GlintIntermediate

Exporting raw response data from surveys for Kirkpatrick Level 1 ('smile sheet') analysis. You'll also be involved in designing effective surveys and beginning to analyse open-text feedback.

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 MethodologyFollow established guidelines and templates. Escalate complex or novel data issues to your supervisor.Independently choose the most efficient method (e.g., Power Query vs. Excel formulas) for routine cleaning. Propose new methods for recurring messy datasets to your Senior Analyst.Design and implement new, scalable data cleaning pipelines. Define best practices and mentor junior team members on advanced techniques.
Dashboard Design & PublicationUpdate existing dashboards following templates. Any new visualisations require approval from your supervisor.Design and build new standard dashboards from requirements, choosing appropriate visualisations. Publish to a development environment for review before wider release.Lead the design of complex, interactive dashboards for strategic initiatives. Manage server permissions and ensure performance optimisation for published dashboards.
Analysis Scope & MethodologyExecute analyses based on clearly defined questions and prescribed methodologies.Propose the most appropriate analytical approach for routine L&D questions (e.g., correlation vs. simple comparison). Escalate if the question requires advanced statistical methods or causal inference.Define the analytical approach for ambiguous, strategic L&D questions. Recommend experimental designs (even quasi-experimental ones) and interpret complex statistical models.
Stakeholder CommunicationRespond to direct data requests. All proactive communication of insights requires review.Communicate routine findings and dashboard updates directly to L&D Programme Managers and HRBPs. Escalate any potentially sensitive or high-impact findings to your Senior Analyst.Lead presentations of complex insights to L&D leadership and cross-functional teams. Act as the primary point of contact for analytics for specific business units.

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.

Report & Dashboard Accuracy
The correctness of the data and calculations in your reports and dashboards.
Target · Fewer than 2 minor data discrepancies per quarter, zero major errors.

You build a dashboard showing course completion rates. If a programme manager spots that 5% of their team's completions are missing, that's a discrepancy. If the total number of learners is off by 20%, that's a major error.

Standard Request Turnaround Time
How quickly you can deliver routine data pulls or updates to existing dashboards.
Target · 90% of standard requests completed within 3 working days.

An HRBP asks for a list of everyone who completed a specific compliance course last month. You should be able to get that to them within three days, assuming the data is readily available.

Data Cleaning Efficiency
The time it takes to prepare raw data for analysis, especially from messy sources.
Target · Reduce average data cleaning time for recurring tasks by 15% over 12 months.

If cleaning the monthly LMS export used to take you 8 hours, we'd want to see that reduced to around 6.5-7 hours through better processes or automation.

Dashboard Usage Rate
How often the dashboards you build are actually viewed by stakeholders.
Target · Average 10+ unique weekly views for each primary dashboard you own.

Your 'Manager Training Impact' dashboard should be regularly checked by L&D managers and HRBPs, not just gathering digital dust.

Clarity of Insights
How well you can explain complex data findings in simple terms to non-technical colleagues.
  • Stakeholders consistently say your explanations are easy to understand. They can repeat the 'so what' of your analysis. You're not using jargon unless absolutely necessary, and if you do, you explain it.
Proactive Problem Identification
Your ability to spot potential issues or interesting trends in the data before being asked.
  • You bring unexpected findings to your manager's attention. You propose new analyses based on what you're seeing. You flag data quality issues and suggest fixes, rather than waiting for someone else to notice.
Stakeholder Feedback on Support
How helpful and responsive colleagues find your analytical support.
  • Positive comments in informal feedback or our annual engagement survey. Colleagues come to you first with data questions because they trust your input and approachability. They feel heard and understood.
Documentation Quality
How well you document your data sources, cleaning processes, and dashboard logic.
  • Another analyst can pick up your work and understand it without constant questions. Your documentation is up-to-date and easy to follow. This helps us all avoid 'Scraping the LMS' hell in the future.

5Would you like it

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

What people enjoy
Solving Puzzles

You get a real kick out of taking a jumbled mess of data and finding the patterns or the root cause of an anomaly. It's like a daily treasure hunt where the 'treasure' is a clear insight. You enjoy the process of cleaning, transforming, and connecting disparate datasets.

You're given a report that shows a dip in course completions. Instead of just reporting it, you dig into the LMS logs, cross-reference with HRIS data, and discover it's due to a system bug that prevented completions from being recorded for a specific group of users.

Making an Impact

You're driven by the idea that your work helps the business make better decisions about its people. Seeing your analysis lead to a change in a training programme or a new L&D strategy is genuinely rewarding for you. You want your numbers to mean something.

Your analysis shows that a particular onboarding programme significantly reduces new hire churn. This insight leads to the programme being rolled out company-wide, and you see the positive impact on retention figures.

Continuous Learning & Improvement

You're always looking for a better way to do things, whether it's a more efficient SQL query, a new visualisation technique, or a different statistical approach. You enjoy learning new tools and methods to improve the accuracy and depth of your analysis.

You pick up a new Power Query technique that automates a data cleaning step that used to take you hours, freeing up your time for more complex analysis. You actively seek out online courses or articles on new L&D measurement methodologies.

What frustrates people
  • Spending 60% of your time on data cleaning and preparation, rather than analysis.
  • Stakeholders asking for a 'quick' analysis that actually requires weeks of work.
  • The inability to run true A/B tests because you can't ethically withhold training from a control group.
  • Leadership pivoting to a new 'Flavor of the Month' initiative, rendering your previous dashboard work obsolete.
  • Dealing with 'Scraping the LMS' – manually exporting CSVs from clunky systems.
What this role does not give you
  • A perfectly clean, ready-to-analyse dataset handed to you every morning.
  • Complete autonomy over strategic L&D decisions (you're providing the data, not making the call).
  • A role where every single piece of analysis you do gets deployed or acted upon.
  • A quiet, solitary environment with no interruptions (you'll be talking to people a lot).

6Who you work with

Your work directly influences how we allocate our L&D budget and design future learning initiatives. By providing clear evidence of what works and what doesn't, you help us optimise our investment in people development. Get it right, and we build a more skilled, effective workforce. Get it wrong, and we could be wasting significant resources on ineffective training, potentially impacting employee engagement and business performance.

Inside the business
  • L&D Programme Managers (they'll want to know if their courses are hitting the mark)
  • HR Business Partners (they need data to advise their business units)
  • Senior Performance Analytics Assistant (your direct manager, for guidance and review)
  • Head of Learning & Development (they'll be reviewing your dashboards for strategic decisions)
  • IT/Data Engineering Team (you'll need their help getting access to data sometimes)
Outside the business
  • LMS/LXP Vendors (occasionally, you might work with their support teams on reporting issues)
  • Survey Tool Providers (if you're troubleshooting data exports)

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 analysis role, ideally within HR, L&D, or a related business function.
  • Demonstrable experience with data cleaning, transformation, and visualisation using tools like Excel, Tableau, or Power BI.
  • A solid understanding of basic statistical concepts (e.g., averages, percentages, correlation) and how to apply them to business data.
  • Proven ability to communicate data insights clearly, both verbally and in writing, to non-technical audiences.
  • Experience querying databases using SQL (even basic SELECT statements count!).

8What to practise next

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

Advanced SQL & Database Management

As we integrate more data sources and move towards a dedicated Learning Record Store (LRS), your ability to query, clean, and even help model data directly in the database will be crucial. Relying solely on manual exports won't scale.

Window Functions · Common Table Expressions (CTEs) · Indexing & Query Optimisation · Data Definition Language (DDL) Basics

  • This week: Find a free online course on advanced SQL (e.g., on Udemy, DataCamp) and dedicate 2-3 hours to it.
  • This month: Practice writing queries with window functions and CTEs using a public dataset (e.g., Kaggle).
  • Month 2: Ask your Senior Analyst for opportunities to review or contribute to more complex SQL scripts.
  • Month 3: Start thinking about how to profile and clean data directly in the database using SQL, rather than exporting to Excel.

Quick win: Refactor one of your existing multi-step Excel data cleaning processes into a single, more efficient SQL query using CTEs.

Statistical Analysis & Causal Inference (Intermediate)

The business will increasingly demand more robust evidence of L&D impact. Moving beyond simple correlations to quasi-experimental methods will be key to truly 'Tying it to Level 3 / Level 4' and navigating 'Attribution vs. Causation Hell'.

Regression Analysis (Linear & Logistic) · Difference-in-Differences (DiD) · Propensity Score Matching (PSM) · Statistical Significance & P-values

  • This week: Read an introductory article or watch a video on 'quasi-experimental design' in a business context.
  • This month: Take an online course on basic regression analysis (e.g., using Excel's Data Analysis ToolPak or Python's statsmodels).
  • Month 2: Identify a past L&D programme where you could apply a basic DiD analysis (e.g., comparing two similar teams, one with training, one without).
  • Month 3: Present a 'lessons learned' from your quasi-experimental analysis to your Senior Analyst, highlighting the challenges and insights.

Quick win: For your next correlation analysis, explicitly list 2-3 other factors that could also be influencing the outcome, showing you understand the limitations of correlation.

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 stay current on trends and troubleshoot problems.
  • Dedicate time each week to online courses (e.g., DataCamp, Coursera, Udemy) on advanced SQL, Power Query, or statistical methods.
  • Attend webinars or virtual conferences focused on People Analytics or L&D Measurement to learn from industry experts.
  • Seek out opportunities to present your analysis to different internal teams, honing your 'Narrative Builder' skills.

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

Competitors are already using Large Language Models (LLMs) to draft reports and summarise qualitative data in minutes, tasks that used to take hours. Analysts who figure this out will outproduce peers significantly. It's not future-state; it's happening now.

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

Your PlanIllustration

Built for Performance Analytics Assistant

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

  1. Data Analytics PrimerNOCN · covers 7 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
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

Competitors are already using Large Language Models (LLMs) to draft reports and summarise qualitative data in minutes, tasks that used to take hours. Analysts who figure this out will outproduce peers significantly. It's not future-state; it's happening now.

  • Context Windows & Token Limits
  • Temperature Settings
  • RAG Architectures
  • Output Validation & Hallucination Detection
  • Prompt Chaining

Advanced Data Storytelling with Interactive Visualisations

Static charts are becoming less impactful. Stakeholders expect to interact with data, drill down, and explore 'what if' scenarios themselves. Your ability to build these dynamic experiences will differentiate you.

  • User Experience (UX) Principles for Dashboards
  • Advanced Interactivity Features
  • Narrative Flow in Dashboards
  • Performance Optimisation for Large Datasets

What you’ll use

Skills this role draws on

Technical

  • Kirkpatrick & Phillips ROI Models
  • Learning Transfer Analysis
  • Skills Gap & Capability Reporting
  • Experimental Design (Basic)
  • Data Storytelling for L&D

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 (any department)

    2-3 years

    Skills to master

    • Data cleaning and transformation, basic SQL querying, dashboard building (Tableau/Power BI), clear communication of findings.

    You're ready to move on when

    • You can independently build a dashboard from raw data to a presentable format.
    • You're the go-to person for complex Excel formulas or basic SQL queries in your current team.
    • You've taken ownership of at least one recurring data reporting process.
  2. 2

    L&D Coordinator / Specialist (with a data bent)

    3-4 years

    Skills to master

    • Deep understanding of L&D processes and systems (LMS/LXP), experience with 'Smile Sheets' and basic programme evaluation, strong Excel skills.

    You're ready to move on when

    • You're constantly trying to measure the impact of L&D programmes, even if it's just with basic data.
    • You're frustrated by the lack of good data in L&D and want to fix it.
    • You're proficient in extracting data from an LMS and summarising it in Excel.
  3. 3

    Business Analyst (with a focus on reporting)

    2-4 years

    Skills to master

    • Requirements gathering, basic data modelling, reporting tool proficiency, stakeholder communication, translating business needs into data questions.

    You're ready to move on when

    • You're skilled at translating vague business questions into clear data requirements.
    • You've built reports or dashboards that are regularly used by business stakeholders.
    • You understand the business context behind the numbers you're reporting.

11Where this role leads

The long view:Your journey in performance analytics is really what you make of it. Whether you want to become a deep technical expert, a team leader, or a strategic influencer, this role provides a fantastic foundation. We're here to support your growth, but ultimately, your career path is in your hands.

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 Performance Analytics Assistant 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 Performance Analytics Assistant

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 Performance Analytics Assistant

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.

  • Report & Dashboard AccuracyThe correctness of the data and calculations in your reports and dashboards.You build a dashboard showing course completion rates. If a programme manager spots that 5% of their team's completions are missing, that's a discrepancy. If the total number of learners is off by 20%, that's a major error.Fewer than 2 minor data discrepancies per quarter, zero major errors.
  • Standard Request Turnaround TimeHow quickly you can deliver routine data pulls or updates to existing dashboards.An HRBP asks for a list of everyone who completed a specific compliance course last month. You should be able to get that to them within three days, assuming the data is readily available.90% of standard requests completed within 3 working days.
  • Data Cleaning EfficiencyThe time it takes to prepare raw data for analysis, especially from messy sources.If cleaning the monthly LMS export used to take you 8 hours, we'd want to see that reduced to around 6.5-7 hours through better processes or automation.Reduce average data cleaning time for recurring tasks by 15% over 12 months.
  • Dashboard Usage RateHow often the dashboards you build are actually viewed by stakeholders.Your 'Manager Training Impact' dashboard should be regularly checked by L&D managers and HRBPs, not just gathering digital dust.Average 10+ unique weekly views for each primary dashboard you own.
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 Performance Analytics Assistant to Senior Performance Analytics Assistant (L3), and whatever you decide comes after.

Level 3 · in progressAI Fluency→ Senior Performance Analytics Assistant (L3)→ your design
Where this takes you

Your journey in performance analytics is really what you make of it. Whether you want to become a deep technical expert, a team leader, or a strategic influencer, this role provides a fantastic foundation. We're here to support your growth, but ultimately, your career path is in your hands.

See Your Progress GrowIllustration
Performance Analytics Assistant
  • Kirkpatrick & Phillips ROI Models
  • Learning Transfer Analysis
  • Skills Gap & Capability Reporting
  • Experimental Design (Basic)
  • Data Storytelling for L&D
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

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

  1. Senior Performance Analytics Assistant (L3)

    2-3 years

    You'll move from owning projects to owning complete workstreams, tackling more ambiguous problems, and mentoring junior colleagues. Your decision authority on technical matters will increase significantly.

    • Advanced SQL (window functions, CTEs, query optimisation)
    • Basic Python/R for statistical analysis (e.g., regression)
    • Experimental Design & Quasi-Experimental Methods (applying DiD, PSM)
    • Data Governance & Quality Assurance (leading efforts to improve data cleanliness)
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a big chunk of data work is often repetitive and time-consuming. But what if you could offload some of that grunt work to AI? At Zavmo, we're not just talking about AI; we're actively integrating it into our daily workflows to free up our analysts for the really interesting stuff – the deep thinking, the problem-solving, and the storytelling.

As a Performance Analytics Assistant, you'll be at the forefront of this. We're talking about using AI not to replace your job, but to make it genuinely more productive and, frankly, more enjoyable. Imagine cutting down on tedious data cleaning or drafting reports in minutes instead of hours. That's the reality we're building, and you'll be a key part of it.

Thematic Analysis Accelerator

Feed thousands of open-ended comments from course feedback surveys ('Smile Sheets') into an LLM. It'll instantly categorise feedback into themes like 'Instructor Quality,' 'Content Relevance,' and 'Platform Issues,' saving you hours of manual tagging. You'll then focus on interpreting the themes, not creating them.

Automated Narrative Generation

Imagine connecting an AI tool to your Tableau or Power BI dashboard. It could auto-generate a first draft of your weekly or monthly narrative summary, highlighting key trends, outliers, and deltas. You'll then refine it, adding your unique insights and the human touch, rather than starting from a blank page.

Stakeholder Comms Drafter

Got a key finding, like 'Our new manager coaching programme is correlated with a 15% higher team engagement score'? Provide an LLM with this insight and ask it to draft a clear, jargon-free email to a business leader explaining what this means and why it matters. It's a massive head start on your 'Narrative Builder' responsibilities.

SQL Query Co-Pilot

Use AI assistants (like GitHub Copilot) to translate natural language requests – 'Show me all employees who completed the sales training and their quota attainment for the next quarter' – into a valid SQL query. This accelerates your data exploration and helps you learn complex SQL faster, reducing time spent debugging.

Common questions

Common questions

How do you become a Performance Analytics Assistant?

Common routes in include Junior Data Analyst (any department) (2-3 years), L&D Coordinator / Specialist (with a data bent) (3-4 years) and Business Analyst (with a focus on reporting) (2-4 years). Times vary with prior experience.

Where can a Performance Analytics Assistant progress to?

This role can lead on to Senior Performance Analytics Assistant (L3) (2-3 years), depending on the skills you build.

What level is a Performance Analytics Assistant 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 Performance Analytics Assistant?

Increasingly, Prompt Engineering & LLM Integration and Advanced Data Storytelling with Interactive Visualisations. 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 Performance Analytics Assistant, 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 Performance Analytics Assistant: 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 gain here are highly transferable. You could move into broader People Analytics roles, or even transition into Data Science, Business Intelligence, or Strategy roles in other departments or industries. The ability to translate data into business insights is valuable everywhere.

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.