United Kingdom · Technical roles · Lead (8-12 years)

Lead Data Visualisation Engineer

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 bandLead (8-12 years)
Direct reports3-8 reports
Reports toManager, Business Intelligence
UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Data Visualisation Architect · Principal BI Developer · Senior BI Engineer · Analytics Platform Lead

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 Lead Data Visualisation Engineer

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

You're the person who designs how our data actually gets seen and understood. This isn't just about making pretty charts; it's about building the underlying architecture and frameworks that make our data accessible, reliable, and performant for everyone else. You'll be setting the standard, mentoring the team, and making sure our BI solutions can handle what the business throws at them.

2What you'd actually use

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

Tableau Desktop/CloudExpert

Designing and building complex, performant dashboards; mastering LOD expressions; managing Tableau Cloud sites; mentoring team on best practices.

Power BI Desktop/ServiceExpert

Developing sophisticated data models with DAX; implementing row-level security; managing Power BI Service workspaces; providing expert guidance to the team.

SQL (PostgreSQL, T-SQL, BigQuery SQL)Architectural

Writing and optimising complex CTEs, window functions, and stored procedures; designing performant views for BI consumption; advising data engineering on table structures.

Snowflake / Google BigQueryExpert

Designing and architecting schemas for analytical workloads; managing costs and performance; advising on data sharing strategies for BI.

Python (pandas, Matplotlib, Seaborn)Strategic

Writing custom scripts for complex data transformation and cleaning; integrating Python into data pipelines for bespoke visualisations; championing programmatic approaches.

Confluence / JiraAdministrator

Designing the Confluence/Jira workflow for the entire analytics function; creating structured documentation templates; managing the BI request backlog and sprint prioritisation.

Excel (including Power Query)Strategic

Understanding when to migrate Excel-based processes to enterprise BI platforms and leading the transition; using Power Query for complex data ingestion and transformation for ad-hoc analysis.

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 choice	Coming in	Where you are now	The step above
Technical Approach for Dashboard Development	Follows established templates and best practices; seeks guidance on deviations.	Selects appropriate tools/methods from approved list; consults on complex choices.	Designs novel solutions and establishes new best practices; consults on strategic implications.
Data Model Design & Optimisation	Works within existing data models; flags potential performance issues.	Proposes minor optimisations to existing models; designs simple new models.	Architects complex dimensional models for new data sources; leads performance tuning initiatives.
Team Mentorship & Task Allocation	Receives tasks and guidance from senior team members.	Provides informal guidance to new joiners; manages own task prioritisation.	Delegates tasks to direct reports, coaches on technical challenges, conducts performance reviews.
Budget Allocation (Project-Specific)	No budget authority; flags resource needs to supervisor.	Recommends software purchases or training up to £5K; requires manager approval.	Authorises project-specific software or training up to £50K; consults on larger spends.

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.

Dashboard Performance & Load Times

The average load time for our critical, high-traffic dashboards.

Target · Average load time under 5 seconds for top 20 dashboards.

If the Sales Performance dashboard takes 12 seconds to load, you'll be expected to diagnose and fix the underlying data model or query issues to get it under 5 seconds.

Data Model Efficiency & Scalability

The efficiency of data models you design, measured by query costs and resource usage in our data warehouse.

Target · Reduce average query costs for BI workloads by 15% year-on-year.

Your new dimensional model for customer data should reduce the average BigQuery cost per dashboard refresh by 20% compared to the old flat-table approach.

Self-Service Adoption Rate

The percentage of business users actively engaging with self-service features (e.g., creating their own ad-hoc reports from certified data sources).

Target · Achieve >75% weekly active users on key self-service dashboards and data sources.

If only 30% of our sales team are using the 'Sales Explorer' dashboard for their own queries, you'll need to figure out why and improve the underlying data model or user experience.

Team Mentorship & Skill Development

The growth and capability improvement of your direct reports and other junior team members.

Target · At least one mentored analyst is promoted or takes on a lead project role within 12 months.

You'll be coaching a junior analyst on advanced SQL window functions. Success means they can then independently write and optimise those queries for a new project.

Architectural Design Quality

The robustness, maintainability, and forward-thinking nature of the BI solutions and data models you design.

Data Engineering team proactively seeks your input on new data pipeline designs. New dashboards built on your models are easily extended by junior analysts. Solutions are rarely re-architected within 18 months due to unforeseen issues. Your documentation is clear and comprehensive.

Stakeholder Trust & Influence

How much key business and technical stakeholders trust your recommendations and involve you in strategic planning.

You're regularly invited to early-stage project planning meetings, not just when a dashboard is needed. Stakeholders explicitly ask for your opinion on complex data challenges. You're seen as the go-to expert for anything related to data visualisation and BI architecture.

Documentation & Best Practices

The clarity, completeness, and adherence to best practices in the documentation for data models, dashboards, and BI processes.

New team members can onboard quickly by following your documentation. The team consistently follows the architectural patterns and coding standards you've established. Auditors or external consultants praise the quality of our data lineage and definitions.

5Would you like it

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

What people enjoy

Building Scalable, Robust Systems

You'll spend your days designing efficient data models, optimising complex queries, and setting up governance frameworks that ensure our BI solutions can handle growing data volumes and user demands without breaking a sweat.

You're excited by the challenge of re-architecting our core sales data model to support 100+ daily users and reduce refresh times by 50%.

Mentoring & Enabling Others

A significant part of your role involves coaching junior analysts, reviewing their code, helping them unstick tricky problems, and guiding them towards best practices in data visualisation and data modelling.

You get a real kick out of seeing a junior team member successfully implement a complex DAX measure after your guidance, and then present it confidently to stakeholders.

Solving Complex Performance Puzzles

You'll be the one diving deep into slow-loading dashboards, diagnosing the root cause (is it the SQL? The data model? The BI tool's calculations?), and implementing elegant solutions to speed things up.

When a critical finance dashboard takes 30 seconds to load, you're the one who eagerly accepts the challenge to get it under 5 seconds, seeing it as a technical puzzle to solve.

What frustrates people

Spending significant time cleaning and validating messy source data, even when you've designed robust pipelines, because upstream systems aren't perfect.
The 'urgent' ad-hoc request from senior leadership that derails your sprint plan for a one-off query that will likely never be used again.
Reconciling your meticulously built, accurate dashboards with a stakeholder's 'spreadsheet truth' that's been manually maintained for years.
Endless revisions and scope creep on a dashboard project, where a simple request for a bar chart morphs into a multi-tabbed 'Franken-board' with 20 filters.
Debating aesthetic choices (like font size or colour palettes) in review meetings when the core analytical insights are being overlooked.
Receiving vague feedback like 'the numbers feel off' or 'this is confusing' without specific, actionable details, forcing you to become a detective.

What this role does not give you

A completely clean, perfectly structured data environment from day one – you'll be part of making it better, but it's a journey.
The ability to ignore stakeholder feedback, no matter how trivial it might seem; user adoption is key.
A purely individual contributor role; you'll be leading and mentoring others, which comes with its own challenges.
A static, predictable environment; the business needs and data landscape are always evolving, so you'll need to adapt.

6Who you work with

This role is absolutely critical for our data-driven decision-making. You'll directly influence the reliability, performance, and user experience of our entire BI landscape. Your work ensures that insights are not just available, but also trusted and acted upon, driving efficiency and strategic advantage across all business units. Frankly, you're building the engine that powers our data understanding.

Inside the business

Manager, Business Intelligence
Head of Data Engineering
Product Leads
Sales Operations Leadership
Finance Business Partners
Senior Analysts across departments

Outside the business

BI Platform Vendors (e.g., Tableau, Power BI)
Data Governance Consultants (occasionally)

7What you need before you start

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

Proven experience (8+ years) in data visualisation, business intelligence development, or a related analytics role, with a clear focus on architectural design.
Demonstrable expertise in at least one major BI platform (Tableau or Power BI), including advanced calculations, data modelling, and performance tuning.
Advanced SQL skills, including writing complex queries, optimising for large datasets, and understanding query execution plans.
Experience leading small teams or mentoring junior analysts, with a track record of fostering skill development.
Strong understanding of dimensional modelling and data warehousing concepts.
A portfolio of complex dashboards or data models you've designed and implemented, showcasing your problem-solving and architectural skills (or equivalent demonstrable work).

8What to practise next

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

Advanced Cloud BI Services & Integration

Critical within 12 months. As cloud platforms evolve, new services (e.g., serverless BI, integrated ML capabilities) are constantly emerging. You'll need to understand how to integrate these for advanced analytics and cost optimisation.

Cloud-Native BI Architectures · Embedded Analytics · Data Streaming for Real-time Dashboards · ML-Powered Augmentation in BI

This month: Take an online course on advanced features of your primary cloud provider's analytics services (e.g., Google Cloud Data Analytics Specialisation).
Month 2: Research and prototype an embedded analytics solution for one of our internal tools.
Month 3: Investigate how real-time data streaming could benefit a specific business process and sketch out a potential architecture.
Month 4: Present a 'future of BI' roadmap to your Manager, incorporating new cloud services.

Quick win: Experiment with the latest features in your primary BI tool (e.g., Tableau Pulse, Power BI Copilot) and share your findings with the team.

9Staying current once you are in

What people here do to keep up

Regularly attend industry conferences (e.g., Tableau Conference, Power BI Summit, Data + AI Summit) to stay current with trends and network.
Contribute to open-source data visualisation projects or maintain a public portfolio of your work on platforms like GitHub or Tableau Public.
Actively participate in online communities (e.g., Kaggle, Reddit's r/dataisbeautiful, LinkedIn groups) to learn from peers and share knowledge.
Take advanced courses or specialisations in data warehousing, cloud data architecture, or advanced analytics from platforms like Coursera, Udacity, or edX.
Mentor junior colleagues or participate in internal knowledge-sharing sessions; teaching is one of the best ways to solidify your own understanding.

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

Critical within 6 months—this isn't just a 'nice to have' anymore. Competitors are already using Large Language Models (LLMs) to draft reports in minutes that used to take hours. Analysts who figure this out will outproduce their peers significantly, and as a Lead, you'll need to guide that adoption.

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

The rising capability

Zavmo analysis

What's rising in its place

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

Prompt Engineering & LLM Integration

Critical within 6 months—this isn't just a 'nice to have' anymore. Competitors are already using Large Language Models (LLMs) to draft reports in minutes that used to take hours. Analysts who figure this out will outproduce their peers significantly, and as a Lead, you'll need to guide that adoption.

Context Windows & Token Limits
Temperature Settings for Tasks
RAG Architectures for Proprietary Data
Output Validation & Hallucination Detection
Prompt Chaining for Complex Analysis

Data Mesh Principles & Decentralised BI

Important within 12-18 months. As our data landscape grows, centralising everything becomes a bottleneck. Understanding data mesh principles will be key to designing scalable, domain-oriented data products and empowering decentralised BI teams.

Data as a Product
Domain-Oriented Ownership
Self-Serve Data Platform
Federated Computational Governance
Interoperability & Standardisation

What you’ll use

Skills this role draws on

Technical

Data Storytelling & Narrative Design
UI/UX for Dashboards
Requirements Gathering & Elicitation
Dimensional Modelling
Dashboard Performance Tuning
Data Governance & Lineage

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
Senior Data Visualisation Analyst (L3)
3-5 years as a Senior Analyst
Skills to master
- Mastering complex DAX/LODs, leading small projects end-to-end, beginning to mentor junior team members, and taking ownership of dashboard performance tuning.
You're ready to move on when
- Consistently delivers complex dashboards with minimal supervision and high accuracy.
- Proactively identifies and resolves performance bottlenecks in existing BI solutions.
- Successfully mentors 1-2 junior analysts, helping them grow their technical skills.
- Actively contributes to defining team best practices and technical standards.
2
Data Engineer (with BI focus)
5-8 years in Data Engineering
Skills to master
- Deep understanding of data pipeline construction, data warehousing, and SQL optimisation, now wanting to specialise in the 'last mile' of data delivery and user experience.
You're ready to move on when
- Has built and maintained robust data pipelines that feed BI systems.
- Demonstrates strong SQL and data modelling skills, with an eye for analytical use cases.
- Expresses a strong interest in user experience and translating data into actionable insights.
- Has some experience working with BI tools from a data preparation perspective.

11Where this role leads

The long view:Your journey here is about continuous growth and impact. Whether you aspire to lead teams, shape technical strategy, or even become a C-level executive, this role provides a robust foundation and a clear path forward. We're here to support your ambition and help you build a truly rewarding career in 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 Lead Data Visualisation Engineer 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.

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 AnalyticsLevel 5

Applied to your work in Lead Data Visualisation Engineer

The objective of this unit is to equip learners with the knowledge and skills to apply data analytics techniques in decision-making processes. Learners will be able to utilise descriptive, statistical, predictive, and prescriptive analytic methods to transform data into actionable insights, forecast future events, and determine optimal solutions for a given situation.

How the thinking builds

Remember
Understand
Apply
Analyse
Evaluate
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.

Your actual work Every lesson is taught against a live piece of your own work, not a worked example from a textbook.
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.
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.
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.
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 Lead Data Visualisation Engineer

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.

Dashboard Performance & Load TimesThe average load time for our critical, high-traffic dashboards.If the Sales Performance dashboard takes 12 seconds to load, you'll be expected to diagnose and fix the underlying data model or query issues to get it under 5 seconds.Average load time under 5 seconds for top 20 dashboards.
Data Model Efficiency & ScalabilityThe efficiency of data models you design, measured by query costs and resource usage in our data warehouse.Your new dimensional model for customer data should reduce the average BigQuery cost per dashboard refresh by 20% compared to the old flat-table approach.Reduce average query costs for BI workloads by 15% year-on-year.
Self-Service Adoption RateThe percentage of business users actively engaging with self-service features (e.g., creating their own ad-hoc reports from certified data sources).If only 30% of our sales team are using the 'Sales Explorer' dashboard for their own queries, you'll need to figure out why and improve the underlying data model or user experience.Achieve >75% weekly active users on key self-service dashboards and data sources.
Team Mentorship & Skill DevelopmentThe growth and capability improvement of your direct reports and other junior team members.You'll be coaching a junior analyst on advanced SQL window functions. Success means they can then independently write and optimise those queries for a new project.At least one mentored analyst is promoted or takes on a lead project role within 12 months.

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 Lead Data Visualisation Engineer to Manager, Business Intelligence (L5), and whatever you decide comes after.

Level 5 · in progressAI Fluency→ Manager, Business Intelligence (L5)→ your design

Where this takes you

Your journey here is about continuous growth and impact. Whether you aspire to lead teams, shape technical strategy, or even become a C-level executive, this role provides a robust foundation and a clear path forward. We're here to support your ambition and help you build a truly rewarding career in data.

See Your Progress GrowIllustration

Lead Data Visualisation Engineer

Data Storytelling & Narrative Design
UI/UX for Dashboards
Requirements Gathering & Elicitation
Dimensional Modelling
Dashboard Performance Tuning
Data Governance & Lineage

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

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

Manager, Business Intelligence (L5)
3-5 years as Lead Data Visualisation Engineer
From L4 to L5, shifting from technical architecture to people and programme management.
- Vendor Management: Evaluating and managing relationships with BI platform vendors and other data tool providers.
- Organisational Design: Structuring the BI team for optimal efficiency and impact.
- Cross-functional Programme Leadership: Leading large, complex BI programmes that span multiple departments.

Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, you've got a lot on your plate. As a Lead Data Visualisation Engineer, you're not just building; you're designing, optimising, and mentoring. The good news? AI isn't here to replace you; it's here to give you back precious hours. Imagine cutting down on the tedious parts of your job, freeing you up for the truly strategic, impactful work.

AI tools are rapidly changing how we approach data visualisation and BI architecture. For a Lead Engineer, this means less time spent on boilerplate code or digging through documentation, and more time on complex problem-solving, innovative design, and high-value mentorship. We're embracing these tools to make our team more efficient and effective.

Rapid Code & Logic Generation

Use AI assistants like GitHub Copilot or ChatGPT to generate complex SQL queries, DAX measures, or Python data cleaning scripts from natural language prompts. This drastically cuts down on development and debugging time, letting you focus on the architectural elegance.

Instant Documentation Drafting

Feed dashboard metadata (fields, filters, calculations) or data model schemas into an LLM to instantly generate a first draft of technical documentation, data dictionaries, or user guides for Confluence. This ensures consistency and saves you hours of tedious writing.

Accelerated Performance Diagnostics

Leverage AI-driven tools within BI platforms (e.g., Tableau's 'Explain Data') or external LLMs to quickly identify potential bottlenecks in dashboard performance, suggest query optimisations, or pinpoint inefficient data model structures. It's like having a super-fast second pair of eyes.

Automated Narrative Generation for Reports

Use built-in AI features (like Power BI's Smart Narratives) or external LLMs to automatically generate plain-English summaries of key insights, trends, and outliers from your dashboards. This helps your team quickly draft reports and ensures consistent messaging for stakeholders.

Common questions

How do you become a Lead Data Visualisation Engineer?

Common routes in include Senior Data Visualisation Analyst (L3) (3-5 years as a Senior Analyst) and Data Engineer (with BI focus) (5-8 years in Data Engineering). Times vary with prior experience.

Where can a Lead Data Visualisation Engineer progress to?

This role can lead on to Manager, Business Intelligence (L5) (3-5 years as Lead Data Visualisation Engineer), depending on the skills you build.

What level is a Lead Data Visualisation Engineer in the UK?

This role aligns to RQF Level 5 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 Lead Data Visualisation Engineer?

Increasingly, Prompt Engineering & LLM Integration and Data Mesh Principles & Decentralised BI. 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 Lead Data Visualisation Engineer, 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 11 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 Lead Data Visualisation Engineer: 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 Zavmo curriculum screen: a three-month plan of nine units, ninety credits, each one an Ofqual-regulated unit the learner can tick or untick before starting. — 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 5

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

Other roles in Technical roles

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

If you leave this industry

The skills you'll develop as a Lead Data Visualisation Engineer are highly transferable across almost any industry. Every company needs to make sense of its data, whether that's in finance, retail, healthcare, or tech. Your expertise in data modelling, performance optimisation, and user-centric design will make you a sought-after professional in the broader data and analytics ecosystem.

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.

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.

Lead Data Visualisation Engineer

What's rising in its place

Prompt Engineering & LLM Integration

Data Mesh Principles & Decentralised BI

Skills this role draws on

Technical

How you actually get there, here

Senior Data Visualisation Analyst (L3)

Data Engineer (with BI focus)

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

Roles this leads to

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

A conversation, not a course

No two people run this the same way

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

Everything else the record holds

The career path, and where it branches

Manager, Business Intelligence (L5)

Where AI is starting to help

Rapid Code & Logic Generation

Instant Documentation Drafting

Accelerated Performance Diagnostics

Automated Narrative Generation for Reports

Common questions

You’ve started things before

Less than one coaching session. Every month.

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