UK

United Kingdom · Technology and data · Lead (8-12 years)

Lead DataOps 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.

See how AI is changing this job, and get the full read by email.

Start my free Data Fluency module

As a Lead DataOps Engineer, a good place to start on Data Mesh Principles & Implementation is Using Data to Direct a Department's Priorities, a free Future Fluency module at Level 5. You'd practise using the data your department already produces to decide where its effort should go.

Free to start, no card.

Keep this job description

Where should we send it?

One email: what AI is changing in this job, the new skills it asks for, where it leads, and where to start free. With a link back here. Nothing else follows unless you confirm your address.

Your employer, or 'self-employed'

Leading a team? Try this with up to five of them for 14 days, free.

  • Experience bandLead (8-12 years)
  • Direct reports3-8 reports
  • Reports toHead of Data Platform
  • UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Staff DataOps Engineer · Principal Data Pipeline Engineer · Senior Platform Engineer (Data Focus)

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

NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 3 of 10 standards
Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 2 of 10 standards
NCFE Level 5 Diploma: Data Engineer NCFE · Level 5 · covers 1 of 10 standards
Level 6 Award in Data-led Decision Making Institute of Sales Professionals · Level 6 · covers 1 of 10 standards
Pearson BTEC Level 5 Higher National Diploma in Digital Technologies 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

Introduction to Data Science and Big Data NCC Education Level 5 Extended Diploma in Computing with Data Science
Apply the Concepts of Data Science to Computer Engineering NOCN Level 5 Diploma in Computer Engineering (Information Technology and Data Science)
Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England
Data engineering principles and foundations NCFE Level 5 Diploma: Data Engineer
Data-led Decision Making Level 6 Award in Data-led Decision Making

The future skills this job leans on

Data Fluency turning numbers into a decision
AI Governance Fluency using AI responsibly
Your Plan Illustration

Built for Lead DataOps Engineer

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

  1. Introduction to Data Science and Big Data NCC Education Limited · covers 3 of 10 standards Level 5
  2. Apply the Concepts of Data Science to Computer Engineering NOCN · covers 2 of 10 standards Level 5
  3. Data Analytics Pearson Education Ltd · covers 2 of 10 standards Level 5
  4. Data engineering principles and foundations NCFE · covers 1 of 10 standards Level 5
  5. Data-led Decision Making Institute of Sales Professionals · covers 1 of 10 standards Level 6
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Lead DataOps 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

  • Data Mesh Principles & Implementation
  • Real-time Data Streaming Architectures
Why each one is rising

The future skills this job leans on

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

What happens when you start

Next, a free account. We build your Lead DataOps Engineer plan as you set up, starting with the free Data Fluency module. No card.

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

Ready to pay? Subscribe for £70 a month

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 Lead DataOps Engineer designs and maintains the infrastructure that moves and manages data across an organisation. This work involves leading complex infrastructure projects and guiding a small team of engineers, shaping how data pipelines are built and run.

4What you'd actually use

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

Apache Airflow Expert

Designing complex, dynamic, and interdependent DAGs; implementing custom operators; optimising scheduler performance; troubleshooting deep-seated orchestration issues.

dbt Core/Cloud Expert

Architecting complex dbt projects; implementing custom macros and tests; optimising model performance and dependencies; setting standards for dbt project structure and governance.

GitLab CI/CD Expert

Authoring and maintaining complex CI/CD pipelines (`.gitlab-ci.yml`) for testing, building, and deploying data assets; designing the overall CI/CD strategy for the data platform.

Terraform Expert

Writing reusable modules from scratch; managing complex state files; designing the entire data infrastructure using Terraform; making key architectural decisions (e.g., VPC structure, IAM policies).

Docker & Kubernetes (EKS/GKE) Advanced

Writing multi-stage Dockerfiles for data applications; deploying applications to a K8s cluster using Helm charts or Kustomize; debugging complex pod failures; contributing to container platform strategy.

Snowflake Advanced

Optimising query performance; implementing robust RBAC policies; managing resource monitors and data sharing; contributing to the Snowflake account structure and cost optimisation.

Great Expectations Advanced

Authoring new 'Expectations' and comprehensive data quality suites; integrating checks into Airflow/dbt pipelines; contributing to the enterprise-wide data quality strategy.

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 Architecture & Tool Selection Follows prescribed architecture; uses approved tools. Chooses appropriate tools from approved list for specific tasks; adapts existing architectural patterns. Recommends new tools/technologies; designs significant components within existing architecture.
Incident Response & Resolution Follows runbooks to diagnose and resolve P3/P4 incidents; escalates P1/P2 immediately. Independently diagnoses and resolves P2/P3 incidents; contributes to runbook improvements. Leads diagnosis and resolution for P1/P2 incidents; designs preventative measures; leads post-mortems.
Team & Project Prioritisation Executes tasks as prioritised by senior team members. Prioritises own tasks within project scope; identifies and flags potential blockers. Prioritises workstreams within a project; helps PM define sprint goals; manages dependencies.
Budget Allocation No budget authority. Suggests cost-saving measures; aware of project budget. Recommends tooling/resource spend up to £5K; monitors project budget.

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.

System Uptime for Critical Data Services
The percentage of time our core data services (like Airflow, dbt Cloud, Snowflake) are fully operational and available.
Target · Achieve 99.9% uptime for critical data services.

If Airflow has 43 minutes of downtime in a month, that's roughly 99.9% uptime. You'll be tracking this closely.

Reduction in Manual Operational Work
The percentage decrease in time spent by the DataOps team on repetitive, manual tasks (e.g., restarting pipelines, manual data fixes).
Target · Automate processes to reduce manual support tickets and operational toil by 25% quarter-on-quarter.

If the team spent 100 hours on manual fixes last quarter, we'd expect that to drop to 75 hours this quarter thanks to your automation efforts.

Project Delivery & Architectural Improvements
The successful delivery of major platform improvement projects, measured by adherence to scope, budget, and timeline.
Target · Lead and deliver at least one major platform improvement project (e.g., new observability framework, cloud migration phase) per half.

Successfully rolling out our new data quality framework across 50% of critical pipelines within 6 months, staying within the allocated £100K budget.

Data Freshness SLO Adherence
The percentage of critical data assets that meet their defined Service Level Objectives for data freshness (i.e., how up-to-date the data is).
Target · 99.9% of critical data assets meet their defined freshness targets.

Our 'Daily Sales' dashboard data must be updated by 9 AM GMT. If it's late once a month, that's still within target. More than that, and we have a problem.

Technical Leadership & Mentorship
Your ability to guide and develop junior engineers, setting technical direction, and fostering a culture of excellence.
  • Regular, impactful code reviews
  • junior engineers actively seeking your advice
  • successful onboarding of new team members
  • positive feedback in 1-to-1s from your direct reports
  • you're the go-to person for tough technical challenges.
Architectural Soundness & Future-Proofing
The quality and foresight of your architectural designs, ensuring they're scalable, maintainable, and adaptable to future business needs.
  • New features are integrated easily
  • minimal refactoring needed for scaling
  • designs are well-documented and understood by the team
  • proactive identification of technical debt and proposals to address it
  • designs stand up to peer scrutiny.
Cross-Functional Influence & Collaboration
How effectively you work with other teams (like Data Science, Analytics, Product Engineering) to understand their needs and integrate solutions.
  • You're regularly invited to planning meetings for data-dependent projects
  • other teams proactively consult you on data-related decisions
  • successful resolution of inter-team dependencies
  • positive feedback from peer leads.
Incident Response & Post-Mortem Quality
Your calm, systematic approach during incidents and the thoroughness of your post-mortems, focusing on root cause analysis and preventative measures.
  • Quick, accurate diagnosis during outages
  • well-structured post-mortem documents that identify clear action items
  • a noticeable decrease in recurring incident types
  • you lead blameless post-mortems that drive learning.

7Would you like it

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

What people enjoy
Building Robust Systems

You get a real kick out of designing and implementing resilient data pipelines and infrastructure that just *works*, day in, day out. Seeing a complex system you've architected run smoothly is deeply satisfying.

Successfully delivering a new, fully automated CI/CD pipeline for dbt models that significantly reduces deployment errors and speeds up releases.

Solving Hard Technical Problems

You thrive on tackling the most complex, multi-system technical challenges. Debugging an intermittent Airflow DAG failure caused by a subtle interaction between Kubernetes and Snowflake isn't a chore; it's a puzzle you're eager to solve.

Diagnosing and fixing a 'schema drift' issue that was silently corrupting a critical financial report, then building automated checks to prevent it happening again.

Mentoring & Growing a Team

You enjoy guiding junior engineers, sharing your knowledge, and seeing them develop their skills. You get satisfaction from helping others unblock their work and grow into stronger technical contributors.

Leading a workshop on advanced Terraform module design for your team, or providing detailed, constructive feedback during code reviews that genuinely helps someone learn.

What frustrates people
  • Upstream Anarchy: A different team changes an API response or a database schema without telling you, causing your ingestion pipeline to fail spectacularly at 3 AM.
  • The 'Human Data Warehouse': Being treated like a query engine by analysts who could write the SQL themselves but find it easier to ask you to 'just pull the numbers.'
  • Inheriting 'Temporary' Scripts': That 'quick Python script' someone wrote in 2018 is now a business-critical process, and you've just been made responsible for its constant failures and lack of documentation.
  • The Cost-Cutting Squeeze: Getting pressure from finance to slash the Snowflake bill while simultaneously getting requests from the business to ingest more data at higher frequencies.
  • YAML/Config Hell: Spending half a day debugging a complex deployment failure only to find the cause was a single misplaced space in a 2,000-line Kubernetes configuration file.
What this role does not give you
  • A quiet, predictable work environment with no urgent interruptions.
  • A role where you only write greenfield code and never deal with legacy systems.
  • An environment where all stakeholders immediately agree on data definitions and priorities.
  • A guarantee that every elegant solution you design will be fully implemented and adopted.

8Who you work with

This role directly impacts the entire organisation's ability to use data effectively. Your work ensures that our data platform is robust enough to handle increasing data volumes and complexity, directly supporting strategic initiatives, new product launches, and operational efficiency across all departments. You're building the engine that drives data-driven decision-making.

Inside the business
  • Head of Data Platform
  • Data Science Leads
  • Analytics Managers
  • Product Engineering Leads
  • Cloud Infrastructure Team
  • Security Operations
Outside the business
  • Key Technology Vendors (e.g., Snowflake, AWS)
  • Open Source Communities (for contributions or issue resolution)

9What you need before you start

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

  • You'll need at least 8 years of hands-on experience in Data Engineering, DevOps, or a related technical role, with a significant focus on building and maintaining data platforms.
  • Proven experience leading technical projects and mentoring junior engineers, with a track record of successful project delivery.
  • Deep expertise in at least one major cloud provider (AWS, GCP, or Azure) and its data-related services.
  • A strong portfolio of work demonstrating complex data pipeline design, IaC implementation, and CI/CD automation.
  • The ability to communicate complex technical ideas clearly and concisely to both technical and non-technical audiences.

10What to practise next

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

Advanced Cloud Cost Optimisation for Data

Cloud spend is one of our biggest costs. As data volumes grow, optimising our Snowflake, S3, and compute costs becomes a strategic imperative, not just a nice-to-have. You'll be expected to drive this.

Snowflake Virtual Warehouse sizing and auto-scalin · S3 lifecycle policies and intelligent tiering · Reserved Instances and Savings Plans for compute · Cost allocation tagging and chargeback models · Monitoring and alerting for cost anomalies

  • This month: Deep dive into our current Snowflake usage patterns and identify the top 3 cost drivers.
  • Month 2: Research and propose 2-3 concrete strategies to reduce our Snowflake compute costs by 10%.
  • Month 3: Implement a cost monitoring dashboard specifically for data platform resources.
  • Month 4: Present a quarterly cost optimisation report to the Head of Data Platform with actionable recommendations.

Quick win: Review your current cloud spend for any obvious wastage (e.g., oversized virtual warehouses, old S3 buckets not being cleaned up).

Advanced Data Security & Privacy Engineering

With increasing data regulations and cyber threats, simply 'knowing about' security isn't enough. You'll need to engineer security and privacy directly into our data platform's foundations, making it resilient by design.

Fine-grained access control (FGAC) implementation · Data masking, tokenisation, and encryption techniq · Secure multi-tenancy patterns for data platforms · Vulnerability management for data infrastructure ( · Privacy-enhancing technologies (PETs) awareness

  • This month: Review our current data access control policies in Snowflake and identify any gaps.
  • Month 2: Research and propose a strategy for implementing dynamic data masking for sensitive PII.
  • Month 3: Conduct a security audit of one of our critical data ingestion pipelines, looking for vulnerabilities.
  • Month 4: Work with the Security team to integrate data platform security into our broader security posture.

Quick win: Ensure all your Terraform configurations for data resources have appropriate IAM policies and encryption enabled by default.

11Staying current once you are in

What people here do to keep up
  • Regularly contributing to open-source data projects or communities (e.g., dbt, Airflow, Great Expectations).
  • Attending industry conferences (like DataOps Summit, KubeCon, AWS re:Invent) and sharing key learnings with the team.
  • Subscribing to and actively reading leading DataOps and cloud engineering blogs and newsletters (e.g., Data Engineering Weekly, The Pragmatic Engineer).
  • Participating in online courses or specialisations in areas like real-time data streaming, advanced cloud architecture, or data governance.
  • Mentoring junior engineers or participating in internal technical guilds.

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.

Data Mesh Principles & Implementation

Essential for future readiness in this role.

  • Data as a Product (DaaP)
  • Domain-Oriented Data Ownership
  • Self-Serve Data Platform
  • Federated Computational Governance
  • Data Contracts and SLAs

Real-time Data Streaming Architectures

Essential for future readiness in this role.

  • Event-Driven Architecture
  • Stream Processing Frameworks (Flink, Spark Streami
  • Message Queues (Kafka, Kinesis, Pub/Sub)
  • Exactly-Once Processing Semantics
  • Schema Registry and Evolution for Streams

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • CI/CD for Data
  • Infrastructure as Code (IaC)
  • Data Observability
  • Data Pipeline Orchestration
  • Data Governance Automation

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    Senior DataOps Engineer (L3)

    2-3 years

    Skills to master

    • Mastering complex data pipeline design, leading small workstreams, effectively mentoring junior engineers, and taking full ownership of significant platform components.

    You're ready to move on when

    • Consistently delivering high-quality, robust data solutions with minimal supervision.
    • Proactively identifying and solving complex technical problems.
    • Receiving positive feedback on mentorship and technical guidance from junior team members.
    • Demonstrating strong communication skills with cross-functional teams.
  2. 2

    Senior DevOps / Platform Engineer

    3-4 years

    Skills to master

    • Translating traditional DevOps principles (CI/CD, IaC, observability) to data-specific challenges, learning data orchestration tools (Airflow, dbt), and understanding data modelling concepts.

    You're ready to move on when

    • Successfully migrated traditional application CI/CD skills to data pipelines.
    • Built and maintained infrastructure for data-intensive applications.
    • Developed a solid understanding of data warehousing concepts and SQL.
    • Demonstrated ability to learn and apply new data-specific technologies quickly.
  3. 3

    Senior Data Engineer

    2-3 years

    Skills to master

    • Deepening expertise in infrastructure-as-code, CI/CD for data, data observability, and moving beyond just building pipelines to owning the platform that runs them.

    You're ready to move on when

    • Consistently building highly performant and reliable data pipelines.
    • Taking initiative to improve the underlying infrastructure of data solutions.
    • Demonstrating strong problem-solving skills for platform-level issues.
    • Expressing a keen interest in automation and operational excellence for data.

15Where this role leads

The long view: This work can lead to managing larger engineering teams, moving into technical architecture, or specialising further as a distinguished engineer.

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 DataOps Engineer 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:

  • Principal DataOps Engineer (L5)
  • Engineering Manager, Data Platform (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 Introduction to Data Science and Big Data, worked against Lead DataOps Engineer.

Introduction to Data Science and Big Data Level 5

Applied to your work in Lead DataOps Engineer

The objective of this unit is to provide learners with a systematic understanding of Data Science and Big Data concepts, including their characteristics and applications. Learners will develop proficiency in data collection, design, and modelling techniques, and will be able to select appropriate tools for data pre-processing and apply analytical techniques to generate insights from data.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 6 Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

See Your Progress Grow Illustration
Lead DataOps Engineer
  • CI/CD for Data
  • Infrastructure as Code (IaC)
  • Data Observability
  • Data Pipeline Orchestration
  • Data Governance Automation
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

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

  1. Principal DataOps Engineer (L5)

    3-5 years

    This is a significant jump, moving from leading a team and architecting solutions to defining the multi-year technical roadmap for the entire data platform. You'll be the highest technical authority, influencing strategy across all of engineering.

    • Enterprise Data Governance Frameworks Designing and implementing organisation-wide data governance policies and automation.
    • Advanced Cloud Financial Operations (FinOps) Driving significant cost optimisation initiatives across the entire cloud data estate.
    • Strategic Orchestration Platform Evaluation Leading the evaluation and selection of next-generation orchestration platforms.
    • Data Mesh Implementation Strategy Defining and driving the strategy for decentralised data ownership and data product enablement.

  2. Engineering Manager, Data Platform (L5)

    2-4 years

    This path shifts your focus from hands-on technical architecture to people leadership and team management. You'll be responsible for the growth, performance, and well-being of a larger team of DataOps Engineers.

    • Team Productivity & Velocity Optimisation Implementing processes and tools to improve team efficiency and delivery speed.
    • Organisational Process Improvement Identifying and addressing bottlenecks in cross-functional data workflows.
    • Vendor Relationship Management Managing relationships with key software and service providers.
    • Project Portfolio Management Overseeing multiple concurrent projects and ensuring alignment with strategic goals.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, DataOps can be a grind. Debugging flaky pipelines, writing boilerplate code, and keeping documentation up-to-date eats into your time. But what if you could offload a significant chunk of that work to AI? We're not talking about replacing you; we're talking about making you ridiculously productive.

At Zavmo, we're all about using AI to augment our engineers, not automate them out of a job. For a Lead DataOps Engineer, this means leveraging cutting-edge AI tools to streamline everything from incident response to infrastructure provisioning, freeing you up to focus on high-level architecture and strategic problem-solving.

AI-Powered Incident Diagnosis

Imagine an AI assistant sifting through logs from Airflow, Kubernetes, and Snowflake during an outage. It correlates events and suggests the root cause: 'Pipeline failure correlates with a 5x increase in API latency from the Salesforce source. Recommend investigating the Salesforce connector.' This saves you precious hours of manual debugging when every minute counts.

Proactive Data Quality Monitoring

Instead of writing endless manual tests, an AI model learns the normal patterns of your key data tables (row counts, null percentages, value distributions). It automatically flags anomalies like a sudden drop in daily active users, catching data bugs before a human even notices. This means fewer data incidents and more reliable insights for the business.

Generative Infrastructure-as-Code

Use an AI chat interface to scaffold new configurations. Prompt: 'Generate a Terraform module for a new Snowflake database named 'marketing_sandbox' with read-only access for the 'analyst' role.' The AI generates the HCL code, which you then review and commit. This drastically reduces the time spent on writing boilerplate infrastructure code, letting you focus on the complex bits.

Automated Documentation & Lineage

An AI tool parses your dbt project and Airflow DAGs to automatically generate and update a data catalogue. It documents table schemas, ownership, and creates a visual graph of the data lineage, ensuring your documentation is never out of date. This frees up countless hours of tedious manual documentation, which, let's be honest, no one really enjoys.

Common questions

Common questions

How do you become a Lead DataOps Engineer?

Common routes in include Senior DataOps Engineer (2-3 years), Senior DevOps / Platform Engineer (3-4 years) and Senior Data Engineer (2-3 years). Times vary with prior experience.

Where can a Lead DataOps Engineer progress to?

This role can lead on to Principal DataOps Engineer (3-5 years) and Engineering Manager, Data Platform (2-4 years), depending on the skills you build.

What new skills matter most for a Lead DataOps Engineer?

Increasingly, Data Mesh Principles & Implementation and Real-time Data Streaming Architectures. 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, cloud architecture, distributed systems, CI/CD, data governance and technical leadership, apply across industries. They can lead to platform engineering, cloud architecture, or product management for data-intensive products in fields such as fintech, e-commerce or healthcare.

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.