UK

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

Lead Analytics 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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As a Lead Analytics Engineer, a good place to start on Prompt Engineering & LLM Integration for Data Workflows 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.

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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.

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

Also advertised as Staff Analytics Engineer · Principal Data Modeller · Data Platform Lead

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 4 of 10 standards
Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 2 of 10 standards
NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 6 of 10 standards
OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 4 of 10 standards
Pearson BTEC Level 4 Higher National Certificate in Computing for England Pearson Education Ltd · Level 4 · covers 3 of 10 standards
Highfield Level 3 Diploma for Team Leaders and Supervisors (RQF) Highfield Qualifications · Level 3 · covers 2 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
Data Analytics Primer NOCN Level 4 certificate for Data Analysts
Data Analysis and Visualisation OTHM Level 7 Diploma in Data Science
Data Analysis Highfield Level 3 Diploma for Team Leaders and Supervisors (RQF)

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
Automation Fluency building AI into how work runs
Systems Fluency seeing the whole picture
Your Plan Illustration

Built for Lead Analytics Engineer

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

  1. Data Analytics Pearson Education Ltd · covers 4 of 10 standards Level 5
  2. Data Analytics Primer NOCN · covers 6 of 10 standards Level 4
  3. Data Analysis and Visualisation OTHM Qualifications · covers 4 of 10 standards Level 7
  4. Data Analysis Highfield Qualifications · covers 2 of 10 standards Level 3
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Lead Analytics 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 Workflows
  • Data Mesh Principles in Practice
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 Analytics 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 Analytics Engineer designs data solutions, sets technical direction, and guides a small team of engineers. The work involves solving complex data problems, keeping data reliable, and helping others build on it well.

4What you'd actually use

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

dbt (data build tool) Core/Cloud Expert

Architecting new dbt projects, designing complex macros, setting up and managing CI/CD pipelines (e.g., using GitHub Actions), and establishing best practices for the entire team. You're the go-to person for dbt.

Snowflake (Data Warehouse) Expert

Designing and implementing optimal schemas (star, snowflake), managing virtual warehouses, security (row-level access), and cost optimisation strategies. You're proficient with Snowpipe, Streams, and performance tuning.

Looker / Tableau (BI & Semantic Layer) Advanced

Designing and building complex LookML models from the ground up, implementing Persistent Derived Tables (PDTs), managing content access, and training analysts on usage. You're defining how our business sees data.

Git (via GitHub/GitLab) Expert

Managing the team's branching strategy (e.g., GitFlow), conducting thorough code reviews, enforcing coding standards, and setting up protected branches. You're ensuring code quality and collaboration.

Airflow / Dagster (Orchestration) Advanced

Authoring complex, idempotent DAGs, implementing dynamic pipelines and custom operators. You'll integrate orchestration with dbt Cloud or dbt Core, making sure our data flows smoothly and reliably.

Great Expectations / Monte Carlo (Data Quality) Advanced

Implementing custom data quality tests (expectations), configuring monitors and alerting for data freshness, volume, and schema changes. You're building the safety net for our data.

Python (pandas, NumPy, SQL Alchemy) Intermediate

Developing custom data transformation scripts, building data validation tools, or interacting with APIs for data ingestion when dbt/SQL isn't enough. You can write clean, testable Python code.

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 & Design Follows established architectural patterns; proposes minor modifications with supervisor approval. Designs and implements solutions for specific projects within existing architectural guidelines; consults on major deviations. Designs and implements complex, multi-domain data models; makes technical decisions within project scope; recommends architectural changes to leadership.
Budget & Resource Allocation No budget authority; requests resources via supervisor. Manages project-specific tool usage (e.g., Snowflake credits); requests new tools/resources via manager. Recommends tool purchases or resource allocation up to £5K; manages project-level compute costs.
Team Leadership & Mentorship Receives mentorship; contributes to team discussions. Provides informal guidance to new joiners; participates in code reviews. Mentors 0-2 junior engineers; leads code reviews; helps unblock team members.
Data Quality & Governance Standards Follows existing data quality procedures; reports data anomalies. Implements data quality tests for owned models; investigates and resolves data issues. Designs and implements data quality frameworks for workstreams; makes recommendations for improving data governance.

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.

Core Data Model Adoption Rate
The percentage of critical business intelligence dashboards and reports that rely solely on the curated, 'single source of truth' data models you've designed and overseen.
Target · >80% of new dashboards and reports built on your models

In Q3, 9 out of 10 new dashboards were built using the `dim_customer` and `fct_orders` models you architected, hitting 90% adoption.

dbt Test Coverage & Data Quality Score
The proportion of critical data models and columns covered by automated dbt tests, alongside the overall health score from our data quality platform (e.g., Monte Carlo).
Target · Increase test coverage from 60% to 90% in key dbt projects; maintain >95% data freshness and schema validity.

You led the initiative to add 200 new tests across 50 core models, boosting coverage from 65% to 85% in six months, and reduced P1 data incidents by 50%.

Data Warehouse Cost Optimisation
The efficiency of our data warehouse spend, particularly Snowflake compute costs, relative to data volume and usage. This means finding smarter ways to process and store data.
Target · Reduce Snowflake compute costs by 15% year-on-year through architectural improvements and query optimisation.

By optimising our incremental dbt models and implementing better clustering keys, you helped reduce our monthly Snowflake bill by £15K, a 17% saving for the quarter.

Team Mentorship & Development
The observable growth and progression of the junior and mid-level Analytics Engineers you mentor and lead.
Target · At least one mentored L1/L2 engineer is promoted or takes on significant lead responsibilities within 18 months.

Your mentorship helped Sarah (Junior AE) take ownership of the entire marketing data domain, leading to her promotion to Analytics Engineer within 15 months.

Strategic Influence & Proactive Problem Solving
You're not just reacting to requests; you're anticipating data needs, identifying potential issues before they become problems, and shaping the data roadmap.
  • You're regularly invited to strategic planning meetings outside your immediate team. You propose new data initiatives or architectural changes without being prompted. Senior leaders seek your opinion on complex data challenges, not just for execution, but for direction. You've got a reputation for spotting issues before anyone else does.
Architectural Soundness & Maintainability
The data solutions you design are robust, easy for others to understand and extend, and built with future scalability in mind. They don't just work today; they'll work next year.
  • New team members can quickly onboard onto your projects. Code reviews on your work are typically focused on minor improvements, not fundamental design flaws. Your solutions rarely require emergency fixes. You've established clear coding standards and data modelling patterns that the team follows.
Cross-Functional Collaboration & Education
You're a trusted partner across the business, able to translate complex technical concepts into understandable terms for non-technical colleagues, and you actively help others use data more effectively.
  • You're the first person Product or Finance call when they have a tricky data question. You've run workshops or created guides that genuinely help analysts and business users. You've successfully mediated disagreements over metric definitions between different departments, getting everyone on the same page.

7Would you like it

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

What people enjoy
Building Foundational Systems

You get a real kick out of designing and implementing robust data models that become the 'single source of truth' for critical business metrics. Seeing your architectural decisions enable others to build great dashboards and analyses is deeply satisfying.

You spent weeks meticulously designing the new `fct_customer_journeys` model, and now the Product team is using it daily to improve conversion rates. That's your kind of win.

Mentoring & Empowering Others

You genuinely enjoy guiding junior engineers, helping them debug tricky SQL, reviewing their dbt models, and seeing them grow into confident, independent contributors. You're motivated by the success of your team.

A junior AE you've been mentoring just successfully led their first complex data modelling project from start to finish. You feel proud of their achievement, knowing you helped them get there.

Solving Complex Architectural Puzzles

The most ambiguous, technically challenging data problems are what you live for. You love diving deep into data lineage, optimising slow queries, or figuring out how to integrate a tricky new data source into our existing platform.

We've got a legacy system with inconsistent data. You're the one who figured out how to build a resilient, idempotent pipeline to bring that data into Snowflake, making it usable for the first time.

What frustrates people
  • Upstream Surprises: An engineering team changes a column name in the production database without telling anyone, causing your entire nightly data pipeline to fail at 3 AM. Then you have to explain why the dashboard is broken.
  • The 'Quick Question' Ambush: A stakeholder asks for 'one quick number' that, in reality, requires you to join four new data sources, define three new metrics, and build a net-new data model. It's never 'quick'.
  • Inheriting 'Tech' Debt: Taking over a dbt project with 1,000+ models, no documentation, circular dependencies, zero tests, and a history of 'quick fixes' that are now critical infrastructure.
  • The Scapegoat: When a key business metric is down, the first assumption from leadership is that 'the data is wrong,' and you have to spend two days proving your pipeline is correct, even when the issue is clearly upstream.
  • Endless Meetings: As a Lead, you'll spend a fair bit of time in meetings—planning, reviewing, unblocking, aligning. If you just want to put your headphones on and code all day, this might not be your jam.
What this role does not give you
  • A quiet, predictable environment where data sources are always clean and requirements never change.
  • A role where you only focus on individual contributions without any leadership or mentorship responsibilities.
  • The luxury of always building 'from scratch' without dealing with existing legacy systems or data debt.

8Who you work with

This role directly shapes the reliability, scalability, and usability of our entire analytical data landscape. Your architectural decisions will influence how quickly and accurately we can answer critical business questions, impacting everything from product development to financial forecasting. You'll be the one making sure our 'single source of truth' actually works, and that's a big deal for a data-driven organisation.

Inside the business
  • Head of Product
  • Head of Engineering
  • Senior Business Leaders (Sales, Marketing, Finance)
  • Data Science Team
  • BI & Reporting Teams
Outside the business
  • Key Technology Vendors (e.g., Snowflake, dbt Labs)
  • Industry Peers (for best practice sharing)

9What you need before you start

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

  • A proven track record of 5+ years as a Senior Analytics Engineer or similar role, demonstrating leadership in designing and implementing complex data models.
  • Extensive experience architecting and building dbt projects from the ground up, including advanced macros, CI/CD, and testing frameworks.
  • Deep expertise in a cloud data warehouse like Snowflake, including schema design, performance tuning, and cost optimisation.
  • Strong experience mentoring junior team members and leading technical initiatives.
  • The ability to translate ambiguous business requirements into clear, actionable data solutions, often involving multiple stakeholders.
  • A solid understanding of data-as-code principles and modern software engineering practices applied to data.

10What to practise next

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

Advanced Data Platform Optimisation

As data volumes and usage grow, raw compute costs can spiral. You'll need to become an expert in squeezing every bit of performance and cost efficiency out of our data warehouse and orchestration tools.

Deep Snowflake Performance Tuning · Cost Management & Governance · Orchestration Efficiency

  • This week: Dive into Snowflake's documentation on cost optimisation and advanced performance features.
  • This month: Conduct a full audit of our top 10 most expensive queries in Snowflake and propose optimisations.
  • Month 2: Implement a new cost-saving feature (e.g., auto-suspension policies, better clustering) and track its impact.
  • Month 3: Develop a proposal for a team-wide 'cost-aware' coding standard for dbt models.

Quick win: Review your most frequently run dbt models. Are they truly incremental? Can any `full-refresh` models be converted? Small changes add up.

Data Contract Definition & Enforcement

Upstream data quality issues are a constant pain. Formalising data contracts between data producers and consumers is becoming essential to prevent surprises and build trust.

Schema Evolution Strategies · Data Quality SLAs · Automated Contract Validation

  • This week: Research tools and frameworks for data contracts (e.g., Apache Avro, Protobuf, specific data contract tools).
  • This month: Identify one critical upstream data source that frequently causes issues. Draft a preliminary data contract for it.
  • Month 2: Work with the upstream engineering team to formalise and implement the data contract for that source.
  • Month 3: Evaluate how we could automate the validation of this contract within our CI/CD pipelines.

Quick win: Start documenting the expected schema and quality of your most critical upstream data sources. Even informal documentation is a step towards a contract.

11Staying current once you are in

What people here do to keep up
  • Regularly contributing to or speaking at industry conferences (e.g., Coalesce, Data + AI Summit) to share knowledge and learn from peers.
  • Actively participating in online data communities (e.g., dbt Slack, Data Engineering Weekly) to stay current and contribute to discussions.
  • Mentoring junior talent, either formally within the company or informally through external programmes.
  • Taking advanced courses or certifications in areas like distributed systems, real-time data processing, or advanced machine learning engineering.
  • Reading key industry books and research papers on data architecture, data governance, and data strategy.

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 Workflows

This isn't 'future' anymore; it's happening now. Competitors are already using Large Language Models (LLMs) to draft complex SQL, generate documentation, and summarise data insights in minutes, not hours. Analytics Engineers who master this will outproduce their peers significantly.

  • Context Windows & Token Limits
  • Retrieval Augmented Generation (RAG)
  • Output Validation & Hallucination Detection
  • Agentic Workflows for Data Tasks

Data Mesh Principles in Practice

As our data landscape grows, centralisation becomes a bottleneck. Data Mesh offers a way to scale data ownership and delivery by treating data as a product. As a Lead, you'll need to understand how to implement these principles, not just talk about them.

  • Domain-Oriented Data Ownership
  • Data-as-a-Product
  • Self-Serve Data Platform
  • Federated Computational Governance

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Dimensional Modelling (Kimball Methodology)
  • ELT (Extract, Load, Transform) Design & Architecture
  • Data Governance & Lineage Strategy
  • Data-as-Code Lifecycle & CI/CD for Data
  • Enterprise Data Quality Frameworks
  • Complex Stakeholder Requirement Translation

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 Analytics Engineer (L3)

    3-5 years as a Senior AE

    Skills to master

    • Mastering complex dbt modelling, leading end-to-end data projects, mentoring junior team members, and effectively translating business requirements into technical solutions.

    You're ready to move on when

    • You consistently deliver high-quality, well-tested data models for complex business domains.
    • You're the go-to person for unblocking technical challenges for your peers.
    • You've successfully led several data projects from inception to deployment.
    • You're proactively identifying areas for data platform improvement without being asked.
  2. 2

    Senior Data Engineer

    3-5 years as a Senior DE

    Skills to master

    • Building and maintaining robust data ingestion pipelines, managing distributed data systems, and a strong understanding of data warehousing concepts. You'll need to pick up more of the semantic layer and business logic translation.

    You're ready to move on when

    • You've built and maintained production-grade data pipelines.
    • You understand the nuances of data ingestion, streaming, and batch processing.
    • You're comfortable with distributed systems and cloud infrastructure.
    • You're keen to move 'closer to the business' and focus more on data modelling for analytics.
  3. 3

    Data Architect (Specialist)

    4-6 years as a Data Architect

    Skills to master

    • Designing enterprise-level data architectures, evaluating new data technologies, and defining data governance frameworks. The transition here would involve more hands-on coding and implementation of those architectural decisions.

    You're ready to move on when

    • You've designed conceptual and logical data models for large organisations.
    • You're familiar with various data architectural patterns (e.g., data lake, data warehouse, data mesh).
    • You can articulate the pros and cons of different data technologies.
    • You enjoy getting into the weeds of implementation and seeing your designs come to life.

15Where this role leads

The long view: This work can lead into data architecture, deeper technical specialisms such as data governance or platform engineering, or leadership roles across a data function.

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 Analytics 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:

  • Analytics Engineering Manager (L5)
  • Principal Analytics Engineer (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 Lead Analytics Engineer.

Data Analytics Level 5

Applied to your work in Lead Analytics 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.

See Your Progress Grow Illustration
Lead Analytics Engineer
  • Dimensional Modelling (Kimball Methodology)
  • ELT (Extract, Load, Transform) Design & Architecture
  • Data Governance & Lineage Strategy
  • Data-as-Code Lifecycle & CI/CD for Data
  • Enterprise Data Quality Frameworks
  • Complex Stakeholder Requirement Translation
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 Analytics Engineer is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Analytics Engineering Manager (L5)

    2-4 years as a Lead AE

    From leading a small team to managing a larger function, including other Leads and Seniors. Focus shifts more towards people management, roadmap planning, and cross-functional leadership.

    • Defining and tracking OKRs (Objectives and Key Results) for the entire Analytics Engineering function.
    • Building and scaling high-performing teams, including hiring and onboarding strategies.
    • Translating executive-level strategy into actionable data initiatives.
    • Managing stakeholder expectations across multiple business units.

  2. Principal Analytics Engineer (L5)

    2-4 years as a Lead AE

    This is a highly technical Individual Contributor (IC) path. You'd be setting the technical vision for the entire data platform, solving the hardest architectural problems, and influencing across the organisation without direct reports.

    • Architecting multi-cloud or hybrid data solutions.
    • Leading complex technical initiatives that span multiple teams or departments.
    • Designing advanced data governance and security frameworks.
    • Evaluating and selecting core components of the data stack for the entire organisation.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a big chunk of an Analytics Engineer's job involves repetitive tasks, boilerplate code, and endless documentation. What if you could offload a significant portion of that grunt work to AI? Imagine freeing up hours every week to focus on the truly interesting, complex architectural challenges and strategic thinking. That's exactly what AI-powered tools are starting to offer.

We're not talking about replacing your job; we're talking about making you ridiculously more productive. Think of AI as your super-powered assistant, handling the tedious bits so you can concentrate on designing elegant data models, optimising pipelines, and mentoring your team. It's about working smarter, not harder, and frankly, staying ahead of the curve.

dbt Model Scaffolding

Use a code generation AI to create the initial SQL and YAML file structure for a new dbt model based on a natural language prompt. It handles the boilerplate (tests, descriptions, meta tags), letting you focus on the complex business logic. This means less time setting things up and more time actually building.

SQL Query Optimisation

Paste a complex, slow-running SQL query into an AI tool. It can analyse the execution plan and suggest improvements, such as reordering Common Table Expressions (CTEs), adding clustering keys, or rewriting joins for better performance in Snowflake. No more staring at `EXPLAIN` plans for hours; let AI give you a head start.

Documentation Autopilot

Point an AI tool at your dbt project. It can automatically generate column and model descriptions based on the SQL logic, upstream sources, and existing documentation, significantly reducing the manual effort of keeping documentation current. Future-you (and your team) will be incredibly grateful.

Stakeholder Translation

Feed a technical explanation of a data model change or a complex data issue into an AI assistant and ask it to generate a clear, non-technical summary for a business stakeholder. It helps bridge the communication gap, ensures everyone understands the impact, and saves you time drafting multiple versions.

Common questions

Common questions

How do you become a Lead Analytics Engineer?

Common routes in include Senior Analytics Engineer (3-5 years as a Senior AE), Senior Data Engineer (3-5 years as a Senior DE) and Data Architect (Specialist) (4-6 years as a Data Architect). Times vary with prior experience.

Where can a Lead Analytics Engineer progress to?

This role can lead on to Analytics Engineering Manager (2-4 years as a Lead AE) and Principal Analytics Engineer (2-4 years as a Lead AE), depending on the skills you build.

What new skills matter most for a Lead Analytics Engineer?

Increasingly, Prompt Engineering & LLM Integration for Data Workflows and Data Mesh Principles in Practice. 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 as a Lead Analytics Engineer carry across industries. They can lead into broader data architecture work, specialisms such as Data Governance or Data Platform Engineering, or a Head of Product role for data products. Strong data leaders are needed across most sectors.

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.