UK

United Kingdom · Technology and data · Senior (5-8 years of relevant experience)

Senior Business Intelligence Support Analyst

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  • Experience bandSenior (5-8 years of relevant experience)
  • Direct reportsNo direct reports
  • Reports toLead BI Support Analyst or BI Support Manager
  • UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Senior BI Analyst (Technical) · Technical Data Insights Specialist · Senior Analytics Engineer (Ops Data)

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 5 of 10 standards
Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 3 of 10 standards
NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 2 of 10 standards
Pearson BTEC Level 5 Higher National Diploma in Cloud Computing Pearson Education Ltd · Level 5 · covers 2 of 10 standards
NOCN Level 5 Certificate in Productivity Improvement Practice NOCN · Level 5 · covers 1 of 10 standards
Pearson BTEC Level 5 Higher National Diploma in Business Pearson Education Ltd · Level 5 · covers 1 of 10 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
Introduction to Data Science and Big Data NCC Education Level 5 Extended Diploma in Computing with Data Science
Business Intelligence Pearson BTEC Level 5 Higher National Diploma in Cloud Computing
Data Analysis in support of Productivity Improvement Projects NOCN Level 5 Certificate in Productivity Improvement Practice

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
Automation Fluency building AI into how work runs
Systems Fluency seeing the whole picture
Your Plan Illustration

Built for Senior Business Intelligence Support Analyst

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

  1. Data Analytics Pearson Education Ltd · covers 5 of 10 standards Level 5
  2. Introduction to Data Science and Big Data NCC Education Limited · covers 2 of 10 standards Level 5
  3. Business Intelligence Pearson Education Ltd · covers 2 of 10 standards Level 5
  4. Data Analysis in support of Productivity Improvement Projects NOCN · covers 1 of 10 standards Level 5
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Senior Business Intelligence Support Analyst?

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

The new skills this role is being asked for

  • Prompt Engineering & LLM Integration for Data Analysis
  • Advanced Data Observability & Monitoring
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 Senior Business Intelligence Support Analyst plan as you set up, starting with the free Data Fluency module. No card.

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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

A Senior Business Intelligence Support Analyst investigates technical operations data to explain why things happen and what can be done about it. The work involves building dashboards and reports that help technical leaders make decisions about code quality and deployment speed. It is a hands-on role, owning a piece of work from the first question through to a polished, accurate answer.

4What you'd actually use

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

Tableau Desktop / Looker Expert

Designing and building complex, interactive dashboards for technical leadership, using advanced features like LOD expressions (Tableau) or Liquid (Looker), and mentoring junior analysts on best practices.

SQL (Snowflake / Google BigQuery) Expert

Authoring complex CTEs, window functions, and highly optimised queries against large datasets in our data warehouse. You'll also be debugging and refactoring others' SQL code for performance and accuracy.

Python (pandas, NumPy) Advanced

Developing scripts for data extraction, cleaning, transformation, and manipulation, especially for data sources that don't have direct connectors or require custom logic. You'll use pandas extensively for data wrangling.

dbt (data build tool) Advanced

Building and maintaining our data models in the data warehouse, ensuring data quality, testing, and version control for all transformed technical data. This is how we keep our data clean and reliable.

Jira, GitHub, Jenkins, Datadog (APIs & Data Models) Advanced

Using APIs to extract raw data from these source systems, understanding their underlying schemas, and knowing the nuances and limitations of the data within each platform. You'll be the expert on what data these systems actually hold.

Confluence & Jira (for BI project management) Advanced

Creating comprehensive documentation for your data models and dashboards, managing your project tasks, and leading sprint planning for your BI workstreams. You'll use these tools to keep everyone informed and organised.

5What you get to decide, and how that grows

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

The choiceComing in Where you are nowThe step above
Data Model Design Proposes minor changes to existing models, requires full review and approval. Designs new simple data models for specific reports, reviewed by senior team members. Designs and implements complex, scalable data models for entire technical domains (e.g., DORA metrics), with peer review for critical components.
Dashboard Creation Builds dashboards from existing data models, following strict templates. Independently builds new dashboards for specific teams, with manager review. Owns and designs complex, interactive dashboards for leadership, including advanced calculations and user experience considerations. Presents to stakeholders for feedback and final sign-off.
Technical Tool Selection (within BI stack) Suggests minor tool features for existing tools. Researches and proposes new features or minor add-ons for existing tools, with manager approval. Evaluates and recommends new technical approaches or tools within the BI stack (e.g., a new dbt package, a different Python library), justifying the choice based on technical merit and business impact. Requires manager consultation for significant changes.
Project Scope & Timelines Estimates task durations, all timeline changes escalated. Manages timelines for individual tasks, escalates project-level changes. Manages timelines for entire workstreams, consults manager on significant delays or scope creep that impact other projects.

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.

Project Completion Rate
The percentage of assigned BI projects (e.g., new dashboards, major report overhauls) completed on time and to specification.
Target · 90% of projects completed within agreed timelines

Delivered the new 'Lead Time for Changes' dashboard for the DevOps team two days ahead of schedule, with all requested features implemented.

Data Accuracy & Reliability
The frequency of data errors or discrepancies found in your reports and dashboards after delivery.
Target · <2 critical data errors per quarter

A stakeholder found a calculation error in a DORA metric report. This would count as one error. You'd then fix it and document the root cause.

Self-Service Adoption & Reduction in Ad-Hoc Requests
How often technical teams use your self-service dashboards instead of asking for custom data pulls, indicating the quality and usability of your solutions.
Target · Reduce ad-hoc requests by 25% for areas covered by your dashboards

After launching the new 'Code Churn' dashboard, ad-hoc requests for that specific metric dropped from 10 per month to 2 per month.

Mentee Development & Impact
The progress and growth of junior analysts you informally mentor, measured by their increased autonomy and skill development.
Target · Mentees show measurable improvement in SQL proficiency and dashboard building within 6 months

Your mentee, after 4 months, can now independently build complex SQL queries and create a full dashboard in Tableau without significant oversight.

Stakeholder Trust & Proactive Insight
How much technical leadership trusts your data and actively seeks your input, rather than just asking for data pulls. This means you're seen as a partner, not just an order-taker.
  • You're invited to Engineering leadership meetings to discuss data trends. VPs ask for your opinion on 'what the data really means' before making decisions. You proactively spot and highlight trends or anomalies before others notice them.
Documentation Quality & Maintainability
The clarity, completeness, and accuracy of the documentation for your data models, queries, and dashboards, making it easy for others to understand and maintain your work.
  • Junior analysts can pick up your work and understand it quickly. Few questions are asked about your data definitions or logic. Your documentation is regularly updated and becomes a reference point for others.
Problem Deconstruction & Solution Design
Your ability to take a vague business question, break it down into solvable data problems, and design robust, scalable solutions.
  • You present multiple options for solving a data problem, outlining pros and cons. Your proposed solutions are technically sound and address the root of the stakeholder's question, not just the surface-level request. You can articulate the trade-offs involved in different approaches.
Cross-Functional Collaboration & Influence
How well you work with other teams (like Data Engineering or Product) to get the data you need or to ensure your insights are acted upon.
  • You're able to get the right data from Data Engineering without constant back-and-forth. Product teams actively use your dashboards in their sprint reviews. You can gently push back on unreasonable requests, offering better alternatives, and still maintain good relationships.

7Would you like it

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

What people enjoy
Solving Complex Puzzles

You'll spend a good chunk of your day untangling messy datasets, debugging complex SQL queries, and figuring out how to combine disparate data sources to answer a tricky question.

An Engineering Director asks why 'Time to Restore Service' has doubled. You'll dive into Datadog, Jira, and incident logs, piecing together the story from multiple systems to find the root cause.

Driving Real Operational Improvement

Your work directly influences how our technical teams operate. You'll see your dashboards used in stand-ups, your insights debated in leadership meetings, and your recommendations leading to actual changes in process or tooling.

You build a dashboard showing the impact of a new CI/CD pipeline. Seeing the 'Lead Time for Changes' drop significantly after its deployment, directly linked to your reporting, is incredibly satisfying.

Mastering Technical Data & Tools

You'll constantly be learning new aspects of our technical systems (Jira, GitHub, Datadog) and honing your skills in SQL, Python, and BI tools to extract, transform, and visualise that data.

You discover a new way to use a window function in SQL to calculate 'Code Churn' more efficiently, or you figure out how to pull a specific metric from a GitHub API that nobody else knew was possible.

What frustrates people
  • Spending 60% of your time cleaning and validating data because engineers use Jira tickets inconsistently or don't follow tagging conventions.
  • The '5-Minute' Urgent Request: An Engineering Director stopping by your desk for a 'quick number' that actually requires three complex joins and derails your entire afternoon.
  • Weaponised Metrics: Seeing dashboards you built being used in performance reviews to punish teams, rather than for coaching and improvement, which can lead to teams trying to game the numbers.
  • Endless Definition Debates: The 'Product vs. Engineering' war over the exact definition of 'bug,' 'story point,' or 'done,' which holds up all reporting.
  • Shadow BI: Discovering a Senior Engineer has built their own complex reporting dashboard in a Google Sheet, which now conflicts with your official source of truth.
  • The Moving Goalposts: A stakeholder approves a dashboard design, only to request a complete structural change the day before it's due to launch because they 'had a new idea.'
  • Repeatedly explaining to brilliant technical minds that just because PR size decreased while bug count went up doesn't necessarily mean one caused the other (causation vs. correlation).
What this role does not give you
  • A perfectly structured, predictable work environment with clear-cut tasks every day.
  • The ability to always see every single piece of your work go straight into production and be acted upon immediately.
  • A role where you only ever work with perfectly clean, pre-modelled data.
  • A job where you don't have to deal with conflicting stakeholder priorities or political debates over data definitions.

8Who you work with

This role directly impacts the efficiency and effectiveness of our entire technical organisation. Your insights help improve DORA metrics, reduce technical debt, and ensure our engineering efforts are aligned with business goals. You're essentially providing the compass for our technical leadership, helping them navigate complex operational challenges.

Inside the business
  • Engineering VPs and Directors
  • Product Managers and Leads
  • DevOps and Platform Engineering teams
  • Data Engineering team (for pipeline health)
  • Junior BI Analysts (for mentorship)
Outside the business
  • Tool vendors (e.g., Jira, GitHub, Datadog support)
  • Industry peers (for benchmarking best practices)

9What you need before you start

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

  • Proven experience (5+ years) in a Business Intelligence, Data Analytics, or Data Engineering role, specifically working with technical or operational data.
  • Demonstrable expertise in SQL, including advanced concepts like CTEs, window functions, and performance optimisation.
  • Strong experience building complex dashboards and reports in either Tableau or Looker, including data modelling and advanced calculations.
  • Practical experience with Python for data manipulation (e.g., pandas) and familiarity with data transformation tools like dbt.
  • A solid understanding of software development processes, DevOps metrics (like DORA), and common technical tools (Jira, GitHub, Datadog).
  • Experience mentoring junior colleagues or leading small technical projects.

10What to practise next

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

Advanced Python for Data Engineering & Automation

As data volumes grow and requirements become more complex, Python will be increasingly used for more sophisticated data extraction, transformation, and loading (ETL) tasks, moving beyond simple pandas scripts to more robust, production-ready code.

Object-Oriented Programming (OOP) in Python · API Integration Best Practices · Testing & Debugging Python Code · Basic Cloud Functions/Serverless Compute

  • This week: Refactor one of your existing Python scripts to use functions and classes more effectively.
  • This month: Complete an online course or tutorial on advanced Python for data engineering.
  • Month 2: Build a new data extraction script that uses proper error handling and logging for an external API.
  • Month 3: Explore deploying a simple Python script as a cloud function to automate a small data task.
  • Month 4: Contribute to a shared Python library for common data utilities within the team.

Quick win: Start using `try-except` blocks in your Python scripts today to handle potential API errors gracefully. It's a small change, but it makes your code more robust.

Cloud Data Warehousing Optimisation

Our data warehouse costs can quickly escalate with inefficient queries or poor data model design. Optimising our usage of Snowflake or BigQuery will become increasingly important for cost control and query performance at scale.

Cost Optimisation Strategies (e.g., Snowflake warehouse sizing) · Advanced Indexing & Partitioning · Query Performance Tuning · Data Materialisation Strategies

  • This week: Review the query history for your most used dashboards and identify the slowest queries.
  • This month: Read the documentation on cost optimisation for Snowflake/BigQuery and identify 3-5 actionable changes.
  • Month 2: Refactor one of your most complex dbt models to use incremental loading or a more efficient materialisation strategy.
  • Month 3: Present your findings on cost savings or performance improvements to the team.
  • Month 4: Take an advanced course on data warehousing performance tuning.

Quick win: Check the `EXPLAIN` plan for your slowest SQL query today and see if you can spot an obvious bottleneck. It's often simpler than you think.

11Staying current once you are in

What people here do to keep up
  • Regularly attending industry meetups and conferences focused on data analytics, DevOps, or technical intelligence.
  • Contributing to open-source data projects or maintaining a public GitHub repository of your data analysis work.
  • Taking advanced online courses on topics like cloud data warehousing, advanced SQL, or Python for data engineering.
  • Participating in internal knowledge-sharing sessions, either by presenting your work or learning from others.
  • Reading relevant industry blogs and research papers to stay on top of emerging trends and best practices in technical data analysis.

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 Data Analysis

Competitors are already using Large Language Models (LLMs) to draft reports, summarise data, and even generate initial SQL queries in minutes. Analysts who master this will significantly outproduce their peers, shifting their value from execution to validation and strategic interpretation. This isn't future-gazing; it's happening now.

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

Advanced Data Observability & Monitoring

As our data landscape grows, ensuring data quality and reliability becomes harder. Proactive monitoring of data pipelines and data quality is becoming critical to maintain trust in our insights. We can't afford to have a dashboard showing stale or incorrect data for days.

  • Data Freshness Monitoring
  • Data Quality Rules & Checks
  • Anomaly Detection in Data Distributions
  • Data Lineage Tools
  • Automated Testing for Data Models

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • DORA Metrics Analysis
  • Agile/Scrum Metrics
  • Data Modelling (Kimball Methodology)
  • Stakeholder Requirements Elicitation
  • Root Cause Analysis (RCA) with Data

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

    From BI Analyst (L2) within Zavmo

    2-3 years at L2

    Skills to master

    • Deepen SQL and data modelling expertise, take ownership of full workstreams, begin mentoring junior colleagues, improve stakeholder communication and presentation skills.

    You're ready to move on when

    • Consistently delivers complex dashboards and reports with minimal supervision.
    • Proactively identifies data quality issues and proposes solutions.
    • Trusted by specific teams to be their go-to data person.
    • Successfully mentored a new joiner or junior analyst.
  2. 2

    From Data Analyst at a Tech Company

    5-7 years of experience

    Skills to master

    • Adapt to our specific technical stack (Snowflake/BigQuery, dbt, Tableau/Looker), understand our internal technical systems (Jira, GitHub data models), and master DORA/Agile metrics.

    You're ready to move on when

    • Strong portfolio of past analytics projects, especially those involving technical or product data.
    • Demonstrated ability to work autonomously and lead projects.
    • Proven experience translating complex business questions into actionable data insights.
    • Comfortable with a fast-paced, technically driven environment.
  3. 3

    From Software Engineer with a passion for Data

    6-8 years of experience (including engineering time)

    Skills to master

    • Transition from coding features to coding for data analysis (SQL, Python for data), develop strong data visualisation and storytelling skills, learn stakeholder management for BI contexts.

    You're ready to move on when

    • Deep understanding of software systems and data generation.
    • Strong SQL and Python skills, even if used in an engineering context.
    • A genuine interest in understanding and improving engineering processes through data.
    • Ability to learn new BI tools and methodologies quickly.

15Where this role leads

The long view: This work can lead into broader data science, data engineering, product analytics, or senior technical intelligence roles that shape how an organisation measures its engineering output.

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 Senior Business Intelligence Support Analyst is actually changing.

Where it leads

Roles this leads to

The record names a next step, but the catalogue has no page for it yet:

  • Lead BI Support Analyst (L4)
  • BI Support Manager (L5)

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 Senior Business Intelligence Support Analyst.

Data Analytics Level 5

Applied to your work in Senior Business Intelligence Support Analyst

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
Senior Business Intelligence Support Analyst
  • DORA Metrics Analysis
  • Agile/Scrum Metrics
  • Data Modelling (Kimball Methodology)
  • Stakeholder Requirements Elicitation
  • Root Cause Analysis (RCA) with Data
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

Senior Business Intelligence Support Analyst is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Lead BI Support Analyst (L4)

    3-5 years as Senior BI Support Analyst

    This is a significant step up, moving from owning workstreams to owning entire programs and potentially managing a small team.

    • Architecting new data models and reporting frameworks for the entire R&D organisation.
    • Evaluating and selecting new BI tools or platforms.
    • Advanced data governance and quality framework design.
    • Mentoring other Senior Analysts.

  2. BI Support Manager (L5)

    4-6 years as Senior BI Support Analyst (or 1-2 years as Lead)

    This is a management role, focusing on people leadership, strategic direction, and overall team performance.

    • Defining the overall BI strategy and roadmap for the Technical_roles department.
    • Managing the BI team's budget (£500K-£2M) and resource allocation.
    • Building and nurturing relationships with other department heads (e.g., Data Engineering, Product Leadership).
    • Representing the BI function in broader company-wide strategic initiatives.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a lot of your time as a BI Analyst can be spent on repetitive tasks. Imagine getting back a full day or two each week to focus on the really interesting, high-impact problems. That's what AI can do for you in this role.

We're not just talking about buzzwords here; we're actively integrating AI tools to make your daily work smoother, faster, and more effective. You'll be at the forefront of using these technologies to transform how we deliver insights in Technical_roles.

SQL & Code Automation

Use AI copilots like GitHub Copilot or advanced LLMs to generate complex SQL queries from plain English descriptions. This means less time writing boilerplate code and more time validating logic and exploring data. You can also get AI to write Python scripts for data cleaning or ETL tasks, freeing you up for more strategic work.

Anomaly Detection & Proactive Alerting

Configure AI models to constantly monitor key technical metrics (like build failures, API error rates, or deployment frequency). Get automatic alerts when something statistically significant goes wrong, allowing you to investigate and flag issues to Engineering leadership before they even realise there's a problem. Be the hero who spots it first.

Automated Documentation & Data Discovery

Imagine AI tools scanning our database schemas and query logs to automatically generate data dictionaries, document data lineage, and explain complex table relationships. This makes it incredibly faster for you (and anyone else) to understand new datasets and onboard onto existing projects without endless digging.

Dashboard Narrative Generation

Use generative AI to draft the 'Key Takeaways' or 'Executive Summary' for your dashboards. This transforms quantitative data into a clear, concise qualitative story, making it much easier for busy leaders to grasp the core insights without having to pore over every chart. It's like having a writing assistant for your data presentations.

Common questions

Common questions

How do you become a Senior Business Intelligence Support Analyst?

Common routes in include From BI Analyst within Zavmo (2-3 years at L2), From Data Analyst at a Tech Company (5-7 years of experience) and From Software Engineer with a passion for Data (6-8 years of experience (including engineering time)). Times vary with prior experience.

Where can a Senior Business Intelligence Support Analyst progress to?

This role can lead on to Lead BI Support Analyst (3-5 years as Senior BI Support Analyst) and BI Support Manager (4-6 years as Senior BI Support Analyst (or 1-2 years as Lead)), depending on the skills you build.

What new skills matter most for a Senior Business Intelligence Support Analyst?

Increasingly, Prompt Engineering & LLM Integration for Data Analysis and Advanced Data Observability & Monitoring. 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 technology and data

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

Skills built in this role, including advanced SQL, data modelling, Python, cloud BI tools and analytical problem solving, transfer well elsewhere. They open paths into data science, data engineering, product analytics, or specialised roles in other industries that value technical operational insight.

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.