UK

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

Senior Synthetic Data Engineer

Here is the whole job, in plain words. What it is, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

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  • Experience bandSenior (5-8 years)
  • Direct reportsNo direct reports
  • Reports toLead Synthetic Data Engineer or Manager, Synthetic Data Engineering
  • UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Lead Data Privacy Engineer · Generative AI Specialist (Data) · Senior Data Anonymisation Engineer · Synthetic Data Scientist

Built on 43,079 real UK job descriptions we analysed · grounded in qualifications employers recognise

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 3 of 5 standards
NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 2 of 5 standards
Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 2 of 5 standards
NCFE Level 5 Diploma: Data Engineer NCFE · Level 5 · covers 1 of 5 standards
NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 5 of 5 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
Data engineering principles and foundations NCFE Level 5 Diploma: Data Engineer
Data Management Software Skills AIM Qualifications Entry 3 Diploma in Skills for Working in Business, Administration and Customer Service Industries
Data Engineering NOCN Level 4 certificate for Data Analysts

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
AI Governance Fluency using AI responsibly
Foresight Fluency seeing round the corner
Systems Fluency seeing the whole picture
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Your Plan Illustration

Built for Senior Synthetic Data Engineer

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

  1. Data Analytics Pearson Education Ltd · covers 3 of 5 standards Level 5
  2. Introduction to Data Science and Big Data NCC Education Limited · covers 2 of 5 standards Level 5
  3. Data engineering principles and foundations NCFE · covers 1 of 5 standards Level 5
  4. Data Management Software Skills AIM Qualifications · covers 1 of 5 standards Entry Level
  5. Data Engineering NOCN · covers 5 of 5 standards Level 4
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 Synthetic Data Engineer?

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 Synthesis
  • Federated Learning for Privacy-Preserving Model Training
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 Synthetic Data Engineer plan as you set up, starting with a Future Fluency module on using AI in your job. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

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3What this role really is

Builds and refines systems that generate synthetic data, artificial data that behaves like real data but without exposing real customer information. This data supports testing, development, and analytics work while keeping sensitive information protected. The role involves making sure synthetic data is statistically sound and useful for the teams who rely on it.

4What you'd actually use

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

Python (PyTorch, Pandas, NumPy, Scikit-learn) Expert

Implementing custom generative models, building data preprocessing pipelines, developing validation scripts, and contributing to internal libraries.

Synthetic Data Platforms (Gretel.ai, YData Fabric) Advanced

Building custom connectors, fine-tuning advanced model parameters (e.g., conditional GANs), evaluating and comparing platform capabilities, and troubleshooting complex generation jobs.

Data Orchestration (Apache Airflow, Dagster) Advanced

Designing and building complex, multi-stage data generation and validation pipelines, implementing dynamic DAG generation, and optimising existing workflows.

Cloud Services (AWS S3, SageMaker, Glue, Snowflake) Advanced

Implementing cost-effective data processing using AWS Glue or Snowpark, optimising Snowflake query performance, managing S3 data storage, and deploying models in SageMaker.

Data Quality & Validation (Great Expectations, Evidently AI) Advanced

Creating new, complex 'Expectations' for data validation, using Evidently AI to diagnose subtle statistical drift between synthetic and real datasets, and building automated quality gates.

Containerisation & MLOps (Docker, MLflow, Kubernetes) Advanced

Writing complex Dockerfiles from scratch, designing and implementing MLflow projects for reproducible generation pipelines, and integrating with Kubernetes for scaling and deployment.

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
Technical Approach for Synthetic Data Generation Follows prescribed methodologies and tools, with all deviations reviewed by a senior engineer. Chooses appropriate generative models and tools for routine problems, escalating novel situations for review. Designs and implements novel generative model architectures and selects appropriate platforms (e.g., Gretel.ai, PyTorch) for complex, non-routine problems, consulting with Lead on strategic implications.
Data Privacy Trade-offs (Fidelity vs. Privacy) Applies predefined privacy settings; escalates any ambiguity to senior engineer or Legal. Makes routine decisions on privacy parameters within established guidelines; escalates exceptions. Makes judgment calls on balancing data utility and privacy for specific datasets, working with Legal & Compliance to define acceptable risk profiles and escalating only high-impact, novel risks.
Project Timelines & Scope Changes Informs supervisor of any potential delays or scope creep immediately. Proposes adjustments to project timelines for routine tasks, seeking manager approval for significant changes. Manages and adjusts project timelines for their workstreams, proactively communicating impacts to stakeholders and consulting with their Lead on major scope changes or resource reallocations.
Mentorship & Technical Guidance Seeks guidance from senior colleagues. Provides informal guidance to new joiners on routine tasks. Actively mentors 1-2 junior engineers, providing structured feedback, technical guidance, and career support; leads code reviews and knowledge sharing sessions.

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.

Synthetic Data Utility Score (TSTR Accuracy)
The accuracy of downstream ML models trained on your synthetic data when tested against real data. This tells us if the synthetic data is actually good enough to replace real data for model training.
Target · Maintain >90% of real-data model accuracy for critical use cases

If a fraud detection model trained on real data gets 95% accuracy, your synthetic data should enable a model to achieve at least 85.5% accuracy (90% of 95%).

Data Generation Pipeline Reliability
The percentage of scheduled synthetic data generation jobs that complete successfully without manual intervention or errors.
Target · >99% success rate for production pipelines

Out of 100 scheduled runs this week, 99 completed perfectly, and one failed due to a schema change you quickly fixed. That's 99%.

Privacy Metric Adherence (e.g., Differential Privacy Epsilon)
Ensuring your generated datasets meet predefined privacy budgets or k-anonymity thresholds, as agreed with Legal & Compliance.
Target · Epsilon < 5 for highly sensitive datasets; k-anonymity > 5 for others

You've proven that the latest customer transaction dataset has an epsilon of 3.5, well within the agreed-upon privacy budget, meaning individual records are extremely hard to re-identify.

Data Provisioning Time Reduction
The average time it takes for a team to get a new, high-quality synthetic dataset from request to delivery.
Target · Reduce average time by 25% compared to previous quarter

Last quarter, it took 8 days on average to deliver a new synthetic dataset. This quarter, you've streamlined the process to 6 days, a 25% reduction.

Technical Leadership & Mentorship
How effectively you guide and develop junior team members, sharing your knowledge and helping them grow their skills.
  • You're regularly sought out by junior engineers for advice. You lead code reviews that genuinely improve others' work. Your mentees show clear progress in their technical abilities and autonomy. You've proactively organised a training session on a new generative model technique.
Cross-Functional Collaboration & Influence
Your ability to work effectively with other teams (like Data Science, Product, Legal) to understand their needs and get them on board with synthetic data solutions.
  • Product Managers are coming to you early in their planning cycles. Data Scientists trust your data and actively use it. Legal & Compliance sees you as a partner, not an obstacle. You've successfully presented a complex technical concept to a non-technical audience and got their buy-in.
Proactive Problem Solving & Innovation
Your initiative in identifying potential issues with data quality or generation techniques and proposing novel solutions before they become major problems.
  • You've identified a subtle 'mode collapse' issue in a GAN and proposed a fix before it impacted any downstream models. You're experimenting with a new diffusion model technique you read about, even if it's not officially on the roadmap yet. You've streamlined a manual process you spotted that was causing delays.
Documentation Quality & Knowledge Sharing
How well you document your work, processes, and findings, making it easy for others to understand, use, and maintain your solutions.
  • Your generation pipelines have clear READMEs and inline comments. New team members can quickly get up to speed on your projects by reading your documentation. You regularly contribute to our internal knowledge base with useful guides and best practices.

7Would you like it

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

What people enjoy
Solving Hard, Novel Technical Challenges

You'll be tackling problems that don't have off-the-shelf solutions, like generating realistic time-series data for financial transactions or ensuring referential integrity across complex database schemas. This means diving into academic papers, experimenting with new models, and pushing the boundaries of what's possible.

Spending a week trying to implement a new diffusion model for tabular data, even if it's just a proof-of-concept, because you believe it could solve a long-standing data diversity issue.

Making a Tangible Impact on Data Privacy & Innovation

Your work directly enables other teams to innovate faster and safer. You'll see your synthetic datasets being used to test new product features, train critical machine learning models, and ensure our compliance with strict data regulations. It's about building the foundation for secure, data-driven growth.

Successfully delivering a synthetic version of our core customer database that passes all privacy audits, allowing the product team to onboard new developers without needing access to real PII.

Continuous Learning & Mastery in a Niche Field

The synthetic data space is constantly evolving. You'll be expected to stay on top of the latest research, tools, and techniques, becoming a true expert in a highly specialised and in-demand area. This means dedicated time for learning and experimentation.

Taking an online course on advanced generative modelling or attending a virtual conference on privacy-enhancing technologies, then bringing those learnings back to the team.

What frustrates people
  • Constant justification: proving to skeptical data scientists and product managers that the synthetic data is statistically sound and 'safe' to use.
  • The moving target problem: production database schemas changing without warning, breaking generation pipelines.
  • Compute budget battles: fighting for GPU resources against 'more important' production ML model training.
  • Debugging black boxes: trying to figure out why a GAN is generating bizarre outputs or failing to learn a key correlation.
  • The 'Just Use Faker' argument: repeatedly explaining that random data isn't statistically representative.
  • Legal & Compliance hurdles: navigating stringent, sometimes technically naive, requirements that can cripple data utility.
What this role does not give you
  • A purely academic research role: while you'll do research, the goal is always practical application.
  • A 'set it and forget it' environment: the data landscape and privacy regulations are always changing.
  • Guaranteed deployment for every model: some experiments won't make it to production, and that's okay.
  • Complete isolation: you'll need to work closely with many different teams.

8Who you work with

This role directly impacts our ability to innovate safely and quickly. By providing high-quality synthetic data, you reduce the risk of data breaches, accelerate development cycles, and enable more robust machine learning model training without touching sensitive production data. Basically, you're building the engine that lets us move fast without breaking things, especially when it comes to privacy.

Inside the business
  • Data Science Team (they'll use your data)
  • Product Managers (they need data for testing features)
  • Legal & Compliance (they'll want to know it's safe)
  • Software Development Teams (they need test data for new features)
  • Junior Synthetic Data Engineers (your mentees)
  • Cloud Operations Team (they'll help with infrastructure)
Outside the business
  • Synthetic Data Platform Vendors (e.g., Gretel.ai, YData Fabric)
  • Privacy Technology Researchers (staying on top of the latest)

9What you need before you start

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

  • At least 5 years of hands-on experience in data engineering, machine learning engineering, or a closely related technical role.
  • Proven track record of building and deploying production-grade data pipelines, ideally involving complex data transformations or machine learning models.
  • Strong programming skills in Python, including experience with data science libraries (Pandas, NumPy) and at least one deep learning framework (PyTorch or TensorFlow).
  • Solid understanding of statistical concepts and their application to data analysis and validation.
  • Experience working with cloud platforms (preferably AWS) for data storage, compute, and orchestration.
  • A degree in Computer Science, Statistics, Mathematics, or a related quantitative field, or equivalent practical experience that demonstrates a deep understanding of these areas.

10What to practise next

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

Advanced Generative Model Architectures & Theory

The field is constantly evolving with new architectures (e.g., latent diffusion models, flow-based models) that offer better fidelity, privacy, or training stability. You'll need to move beyond just implementing, to truly understanding the mathematical underpinnings and knowing why certain models perform better in specific scenarios.

Score-based generative modelling · Information theory in generative models · Bias detection and mitigation in generative models · Architectural design for multimodal synthetic data

  • This quarter: Select two recent academic papers on advanced generative models and present their core ideas to the team.
  • Next 6 months: Implement a novel generative model architecture from scratch (or a significant modification) and benchmark its performance against existing solutions.
  • Within 12 months: Propose a new research direction for our synthetic data team based on emerging architectural trends.
  • Ongoing: Participate in online courses or workshops focused on the theoretical foundations of deep generative models.

Quick win: Subscribe to key ML research newsletters (e.g., 'The Batch' by DeepLearning.AI) and set up alerts for 'generative models' or 'synthetic data' on arXiv. Just staying aware is a great start.

11Staying current once you are in

What people here do to keep up
  • Actively participate in synthetic data or privacy-enhancing technology communities (e.g., GitHub projects, online forums).
  • Regularly read and critically evaluate academic papers from conferences like NeurIPS, ICML, or PETS.
  • Attend industry conferences or workshops focused on generative AI, data privacy, or MLOps.
  • Contribute to open-source projects related to synthetic data or privacy tools.
  • Take advanced online courses on deep learning, Bayesian statistics, or differential privacy from platforms like Coursera, edX, or Udacity.

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 Synthesis

Large Language Models (LLMs) are becoming incredibly powerful for generating text, but their application to structured and semi-structured data is rapidly expanding. We'll soon be using them not just for code, but to guide and augment our synthetic data generation processes, especially for complex, contextual data.

  • Context windows and token limits for data
  • Few-shot and zero-shot learning for data patterns
  • RAG (Retrieval Augmented Generation) for proprietary data
  • Output validation and hallucination detection in data

Federated Learning for Privacy-Preserving Model Training

While synthetic data helps with privacy, sometimes you need to train models directly on decentralised, sensitive real data without it ever leaving its source. Federated learning is becoming critical for scenarios where data can't be centralised, and it's a natural extension of our privacy focus.

  • Secure aggregation techniques
  • Homomorphic encryption basics
  • Differential privacy in federated settings
  • Model poisoning and defence mechanisms

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Generative Modelling (GANs, VAEs, Diffusion Models)
  • Privacy Enhancing Technologies (Differential Privacy, k-Anonymity)
  • Statistical Similarity & Utility Metrics
  • MLOps for Synthetic Data
  • Data Anonymisation & Pseudonymisation
  • Cloud Data Architecture (AWS focus)

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 Mid-Level Data Engineer

    2-3 years of dedicated learning and project work

    Skills to master

    • Deep dive into generative modelling (GANs, VAEs), practical application of privacy-enhancing technologies, and building robust MLOps pipelines for data. You'll need to move beyond just moving data to transforming it in a statistically meaningful and private way.

    You're ready to move on when

    • You've successfully built and deployed at least one complex data pipeline that involves advanced transformations.
    • You've taken initiative to learn about generative AI and tried building a simple GAN or VAE in your spare time.
    • You can articulate the basic trade-offs between data utility and privacy in a data context.
    • You've mentored a junior engineer or contributed significantly to team best practices.
  2. 2

    From Mid-Level Machine Learning Engineer

    1-2 years of focused data privacy and engineering work

    Skills to master

    • Stronger data engineering fundamentals (orchestration, cloud data services), deep understanding of statistical similarity metrics, and the legal/ethical landscape of data privacy. You'll need to shift from building predictive models to building generative ones that also preserve privacy.

    You're ready to move on when

    • You've successfully deployed and monitored several ML models in production.
    • You have a solid grasp of Python for data manipulation beyond just ML frameworks.
    • You're curious about data privacy and have explored concepts like differential privacy.
    • You can design and execute experiments to compare model performance.
  3. 3

    From Data Scientist (with strong engineering skills)

    2-3 years of building production-grade systems

    Skills to master

    • Moving beyond exploratory analysis to building robust, production-ready data generation pipelines. This means mastering MLOps, cloud infrastructure, and the specific engineering challenges of scalable synthetic data. Less ad-hoc scripting, more resilient system design.

    You're ready to move on when

    • You're not just building models; you're comfortable deploying them and thinking about their lifecycle.
    • You've got a good handle on SQL and data warehousing concepts.
    • You can write clean, testable Python code for production systems.
    • You're comfortable presenting complex statistical findings to non-technical audiences.

15Where this role leads

The long view: This work can lead into deeper technical specialism, into managing people, or into shaping data strategy at a senior level.

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 Synthetic Data Engineer is actually changing.

Where it leads

Roles this leads to

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 Synthetic Data Engineer.

Data Analytics Level 5

Applied to your work in Senior Synthetic Data Engineer

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.

Start my Senior Synthetic Data Engineer plan, free
See Your Progress Grow Illustration
Senior Synthetic Data Engineer
  • Generative Modelling (GANs, VAEs, Diffusion Models)
  • Privacy Enhancing Technologies (Differential Privacy, k-Anonymity)
  • Statistical Similarity & Utility Metrics
  • MLOps for Synthetic Data
  • Data Anonymisation & Pseudonymisation
  • Cloud Data Architecture (AWS focus)
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 Synthetic Data Engineer is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Lead Synthetic Data Engineer (L4)

    3-5 years in the Senior role

    This is a significant step up, moving from owning workstreams to shaping the technical direction for multiple workstreams or a small team. You'll be accountable for broader outcomes.

    • Architecting scalable, fault-tolerant synthetic data platforms.
    • Defining enterprise-wide data generation patterns and best practices.
    • Evaluating and selecting new synthetic data platforms or technologies at a strategic level.
    • Leading incident response for major data generation issues.

  2. Synthetic Data Engineer Manager (L5)

    5-7 years in Senior/Lead roles

    This path moves you into people management, leading a team of synthetic data engineers. It's less about individual technical contribution and more about building and nurturing a high-performing team.

    • Organisational design for data engineering teams.
    • Vendor management and contract negotiation for synthetic data platforms.
    • Managing budgets and resource allocation for an entire team or function.
    • Representing the team's work and needs to senior leadership.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be honest, parts of data engineering can be a bit of a grind. But what if you could offload some of that tedious work to AI? Imagine spending less time debugging boilerplate code or drafting validation reports, and more time on the really interesting stuff – like designing novel generative models or solving complex privacy challenges.

As a Senior Synthetic Data Engineer, you're already at the forefront of AI. Now, it's about using AI to make you more productive. Our internal AI Hub offers tools and guidance to help you automate the mundane, accelerate your research, and communicate your complex work more effectively. It's not about replacing you; it's about giving you superpowers.

Code Automation & Debugging

Use AI coding assistants (like GitHub Copilot or Tabnine) to generate boilerplate Python code for data loading, preprocessing, or even initial generative model architectures. Get instant suggestions for debugging complex PyTorch errors or optimising your Pandas operations. It's like having a pair programmer who knows every library inside out.

Automated Quality Report Generation

Feed statistical outputs from your validation tools (e.g., Great Expectations reports, TSTR results, propensity scores) into an LLM agent. It'll synthesise the findings and generate a clear, human-readable summary for non-technical stakeholders, highlighting key quality metrics, privacy adherence, and potential risks. No more spending hours writing up reports.

Novel Architecture Research & Scaffolding

Struggling to keep up with the latest academic papers on tabular diffusion models or advanced GANs? Use an LLM to research, summarise, and compare cutting-edge techniques. Even better, get the AI to generate boilerplate Python/PyTorch code as a starting point for your experiments, saving you days of initial setup.

Stakeholder Explanation & Documentation

Complex concepts like 'differential privacy' or 'mode collapse' can be hard to explain. Use an LLM to draft clear, concise documentation, user guides for your internal SDKs, or even analogies to help Legal & Compliance understand the nuances. It frees you up to focus on the technical implementation, not just the explanation.

Common questions

Common questions

How do you become a Senior Synthetic Data Engineer?

Common routes in include From Mid-Level Data Engineer (2-3 years of dedicated learning and project work), From Mid-Level Machine Learning Engineer (1-2 years of focused data privacy and engineering work) and From Data Scientist (with strong engineering skills) (2-3 years of building production-grade systems). Times vary with prior experience.

Where can a Senior Synthetic Data Engineer progress to?

This role can lead on to Lead Synthetic Data Engineer (3-5 years in the Senior role) and Synthetic Data Engineer Manager (5-7 years in Senior/Lead roles), depending on the skills you build.

What new skills matter most for a Senior Synthetic Data Engineer?

Increasingly, Prompt Engineering & LLM Integration for Data Synthesis and Federated Learning for Privacy-Preserving Model Training. These are the skills our record of this role lists as growing.

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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

The skills built in this role apply across industries that handle sensitive data, including finance, healthcare, government, retail and tech. Demand for this kind of expertise continues to grow as more sectors rely on synthetic data.

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 Senior Synthetic Data Engineer plan, free 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.