UK

United Kingdom · Technology and data · Principal/Manager (12-16 years)

Analytics Manager

As an Analytics Manager, you orchestrate the data that drives your company's future.

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

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

Start my free Data Fluency module

As an Analytics Manager, a good place to start on Prompt Engineering & LLM Integration for Analytics 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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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.

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  • Experience bandPrincipal/Manager (12-16 years)
  • Direct reports10-25 reports
  • Reports toDirector of Analytics
  • UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Head of Analytics · Senior Manager, Data & Insights · Lead Analytics Manager

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 7 of 10 standards
OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 6 of 10 standards
Pearson BTEC Level 4 Higher National Certificate in Computing for England Pearson Education Ltd · Level 4 · covers 3 of 10 standards
BCS Level 4 Diploma - Digital Modular Programme for Data Analysts BCS, The Chartered Institute for IT · Level 4 · 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 BCS Level 4 Diploma - Digital Modular Programme 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
Automation Fluency building AI into how work runs
Your Plan Illustration

Built for Analytics Manager

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 7 of 10 standards Level 4
  3. Data Analysis and Visualisation OTHM Qualifications · covers 6 of 10 standards Level 7
  4. Data Analysis BCS, The Chartered Institute for IT · covers 2 of 10 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 an Analytics Manager?

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

How the AI economy is changing work like this

A broad read on this kind of work, not an analysis of this job on its own. Roles that share a pattern get the same answer here.

Fading: AI does more of this

AI is gradually taking over routine reporting tasks, freeing you from the grind of manual data compilation.

→
Rising: worth more because of AI

Your ability to interpret complex data and translate it into actionable business strategies becomes more valuable than ever.

The new skills this role is being asked for

  • Prompt Engineering & LLM Integration for Analytics Workflows
  • Data Product Management & Monetisation
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 Analytics Manager 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

We see you

You often wonder if AI will ever truly understand the nuanced decisions you make daily. Yet, you value the precision it brings to the endless stream of data at your fingertips.

An Analytics Manager leads a team of analysts and sets the direction for how data is used to answer business questions. The role means turning big questions into analytics projects, then guiding the team to deliver them.

4A day in the life

Not a job advert. A real day, built from what this role actually holds.

08:45
You kick off your day with a team stand-up, setting the tone for the day and aligning on key priorities.
11:00
A meeting with the VP of Engineering, where you present data-driven insights that could influence the next product iteration.
14:30
You review the latest analytics roadmap, ensuring it aligns with the company's strategic objectives and prepares for the upcoming quarter.
16:15
You mentor a junior analyst, guiding them through a complex data model and sharing insights on improving their approach.

5What you'd actually use

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

Snowflake / Databricks Strategic/Architect

Setting the strategy for data ingestion, storage, and access within your domain. Making platform decisions, overseeing cost optimisation, and ensuring robust security and governance policies are in place. You'll be guiding your team on best practices and optimising complex queries.

dbt (data build tool) Strategic/Architect

Defining the enterprise-wide strategy for data transformation and modeling within your functional area. Championing data quality initiatives, overseeing the architecture of new dbt projects, and ensuring CI/CD best practices for testing and deployment are followed by your team.

Tableau / Looker Strategic/Architect

Managing the enterprise BI platform for your domain, defining the strategy for self-service vs. curated reporting. You'll ensure dashboards meet business needs, are performant, and tell a clear story. You'll also guide your team on advanced visualisation techniques and LookML modelling.

Python (pandas, scikit-learn, Plotly) Advanced

Determining when to invest in custom Python-based solutions for predictive models or complex analysis versus using off-the-shelf tools. You'll oversee data science R&D efforts within your team and ensure code quality and maintainability.

Amplitude / Mixpanel Strategic/Architect

Integrating product analytics data with other business data (e.g., CRM, financial) to create a holistic view of the customer. You'll oversee the design and validation of event tracking schemas and ensure these platforms are driving product strategy effectively.

Anaplan / Pigment Advanced

Owning the data models within these executive planning platforms for your domain. You'll collaborate closely with Finance and Operations leadership to build strategic forecasts, 'what-if' scenarios, and ensure data integrity for critical business planning.

Jira, Confluence, Slack Strategic/Architect

Defining and optimising your team's operating rhythm and communication channels using these tools. You'll ensure the analytics roadmap is clearly communicated, key results are tracked, and team collaboration is efficient and transparent.

6What 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
Team Hiring & Structure No authority; provides input on candidate fit. Interviews candidates; provides strong recommendation to hiring manager. Leads interview process; makes hiring recommendations to Manager.
Project Prioritisation & Roadmap Executes assigned tasks; may suggest minor adjustments. Prioritises own tasks within project scope; proposes solutions for conflicts. Owns workstream roadmap; makes technical prioritisation decisions; influences product roadmap.
Budget Allocation (e.g., tools, training) No authority; suggests tools for personal use. Suggests tools/training for own development; needs manager approval. Recommends tooling/training for workstream; budget approval from Manager.
Data Architecture & Governance Follows existing data models and governance rules. Designs new dbt models following best practices; identifies data quality issues. Architects data models for a specific domain; defines data quality standards; leads governance initiatives.

7How 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.

Business Impact Attributed to Analytics
The quantifiable financial benefit (revenue uplift, cost savings) directly linked to insights and models delivered by your team.
Target · Attribute >£5M in incremental revenue or cost savings annually.

Your team's churn prediction model reduces customer churn by 2%, saving £1.2M in potential lost revenue. An A/B test designed by your team increases conversion on a key feature, adding £3.8M in annual recurring revenue.

Time to Insight (TTI) Reduction
Reducing the average time it takes from a business question being asked to a data-driven answer being delivered.
Target · Reduce median TTI by 30% year-over-year.

If it typically took 2 weeks to answer a complex 'why did X happen?' question, your goal is to get that down to 9-10 days through better processes, tooling, and team efficiency.

Company-Wide Data Literacy Score
Improving the overall understanding and comfort level of non-technical stakeholders in using data for decision-making, measured via an internal survey.
Target · Improve the annual data literacy score by 15% across relevant departments.

After your initiatives (training, improved dashboards, better documentation), the average score on the 'Confidence in using data for decisions' question in our annual survey increases from 3.5 to 4.0 out of 5.

Team Engagement & Retention
Maintaining a healthy, motivated, and stable analytics team, measured through engagement surveys and regrettable attrition rates.
Target · Achieve a team engagement score in the top quartile (75th percentile) and maintain <10% regrettable attrition annually.

Your team consistently scores highly on questions about feeling valued, having growth opportunities, and recommending the company as a place to work. Only one team member leaves for reasons we wish they hadn't, out of a team of 15.

Strategic Roadmap Influence
Your ability to proactively shape the company's product and business strategy through data, rather than just reacting to requests.
  • You're regularly invited to strategic planning meetings for Product and Engineering. Your team's insights are explicitly referenced in executive presentations and product roadmaps. You're seen as a trusted advisor, not just a data provider.
Team Development & Mentorship
The growth and progression of your direct reports, both in their technical skills and career paths.
  • Your team members are regularly promoted or take on more complex projects. They express high satisfaction with their growth opportunities during 1:1s and engagement surveys. You've successfully mentored junior team leads.
Data Governance & Quality Leadership
Proactively driving improvements in data quality, consistency, and accessibility across the organisation.
  • You've led initiatives to establish clear data ownership and definitions. Data quality incidents in your domain have decreased. Stakeholders consistently trust the data sources and dashboards your team owns, with minimal 'shadow BI' emerging.
Cross-Functional Collaboration & Partnership
Building strong, productive relationships with other departments, ensuring analytics is integrated into their workflows.
  • Other department heads actively seek out your team's input on new initiatives. There's clear evidence of joint projects with Product, Engineering, and Marketing. You're seen as a bridge-builder, not a silo.

8Would you like it

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

What people enjoy
Driving Tangible Business Impact

You get a real kick out of seeing your team's insights directly lead to a new product feature, a more efficient engineering process, or a measurable increase in revenue. You're always asking 'What's the business outcome here?'

Your team's work on optimising our onboarding funnel directly contributes to a 15% increase in user activation, which you can clearly link to £X in new customer value.

Building and Developing High-Performing Teams

You love the challenge of recruiting top talent, coaching your team members, and watching them grow into more senior roles. You take pride in creating a supportive yet challenging environment where people can do their best work.

You successfully mentor two senior analysts into team lead positions, and your team consistently reports high satisfaction with their career development opportunities.

Solving Complex Organisational Puzzles

You thrive on taking messy, ambiguous business problems, often involving multiple stakeholders and conflicting priorities, and breaking them down into clear, data-driven analytical projects for your team to tackle.

The CEO asks 'Why are our enterprise customers churning more?' You translate this into a multi-month project for your team, defining the data needed, the models to build, and the insights to deliver.

What frustrates people
  • Constantly fighting for engineering resources to improve data quality or instrumentation.
  • Explaining complex statistical concepts to executives who just want simple answers.
  • The 'urgent' last-minute requests that derail planned work.
  • Inheriting a messy, undocumented data infrastructure ('data swamp').
  • Building impactful dashboards that end up in the 'dashboard graveyard' (unused).
  • Dealing with political pressure to spin data to suit narratives.
  • Stakeholders dismissing data inconsistencies as 'directionally correct', undermining precision.
What this role does not give you
  • A purely technical individual contributor path—you're leading people now.
  • A world where data is always clean, complete, and perfectly understood by everyone.
  • A quiet, predictable work environment with minimal interruptions.
  • An environment where every single analytical output directly leads to a major business change.

9Who you work with

This role directly influences the strategic direction and operational efficiency of our technical product development. You'll be instrumental in fostering a data-driven culture, ensuring that investments in product and engineering are backed by solid evidence. Your team's work will drive decisions that impact user acquisition, retention, revenue optimisation, and overall product health across multiple domains, potentially impacting £5M-£20M in annual business value.

Inside the business
  • Director of Analytics (your boss, for strategic alignment)
  • VPs of Product & Engineering (your primary internal clients)
  • Finance Leadership (for budgeting and business case validation)
  • Marketing & Sales Leaders (for cross-functional data needs)
  • Other Analytics Managers (for collaboration and best practices)
Outside the business
  • Key Technology Vendors (e.g., Snowflake, Tableau, Amplitude)
  • Industry Peers (for benchmarking and best practices)
  • Recruitment Partners (for hiring top talent)

10What you need before you start

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

  • Proven experience (typically 8-12 years) as a Lead or Staff Analytics Engineer, or a similar senior individual contributor role, where you've owned complex workstreams and mentored others.
  • Demonstrable experience in designing and implementing scalable data models and analytical systems.
  • A track record of successfully influencing product or engineering roadmaps with data-driven insights.
  • Experience managing projects end-to-end, from ambiguous problem definition to impactful delivery.
  • Strong proficiency in at least one major programming language for data analysis (e.g., Python) and expert-level SQL.

11What to practise next

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

Advanced Data Orchestration & MLOps

As our data pipelines become more complex and we deploy more machine learning models into production, robust orchestration and MLOps practices will be critical. You'll need to understand the principles to guide your team effectively.

Airflow / Prefect for Data Workflows · Model Versioning & Registry · Automated Model Retraining & Monitoring

  • This quarter: Review your team's current data orchestration practices. Identify areas for improvement in reliability and scalability.
  • Next quarter: Sponsor a proof-of-concept for a new MLOps tool or practice within your team.
  • Month 3-6: Develop a roadmap for improving model deployment and monitoring capabilities across your functional domain.

Quick win: Encourage your team to document their existing data pipelines more thoroughly, including dependencies and failure points. This builds a foundation for future automation.

12Staying current once you are in

What people here do to keep up
  • Actively participate in industry conferences and meetups (e.g., Data + AI Summit, Fivetran Modern Data Stack Conference) to stay abreast of emerging trends and network with peers.
  • Enroll in leadership development programmes or executive coaching focused on managing technical teams and influencing senior stakeholders.
  • Contribute to open-source data projects or publish articles/blog posts on analytics best practices and leadership.
  • Mentor aspiring data professionals, both within and outside the organisation, to hone your coaching skills.
  • Regularly engage with product and engineering leadership to understand their challenges and identify new opportunities for analytics to add value.

13The 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 Analytics Workflows

Honestly, competitors are already using large language models (LLMs) to draft reports in 10 minutes that used to take 2 hours. Analysts who figure this out will outproduce their peers significantly. As a manager, you need to guide your team in leveraging these tools safely and effectively.

  • Context Windows & Token Limits
  • RAG (Retrieval Augmented Generation) Architectures
  • Output Validation & Hallucination Detection
  • Ethical AI Use in Data Analysis

Data Product Management & Monetisation

Data isn't just for internal decision-making anymore; it's becoming a product in itself. As we mature, we'll be looking at how to package, deliver, and potentially monetise our data assets. This requires a product mindset within analytics.

  • Data Product Lifecycle
  • User-Centric Data Design
  • Data Monetisation Strategies
  • API Design for Data Products

14What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Experimentation & Causal Inference
  • Data Governance & Lineage
  • Product Analytics Frameworks
  • Predictive Modeling & Forecasting
  • Dimensional Modeling
  • Stakeholder-Centric Roadmapping

15The 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 Lead/Staff Analytics Engineer (L4)

    2-4 years at L4

    Skills to master

    • Transitioning from deep technical expertise to strategic oversight, developing strong people management skills (coaching, performance, hiring), and expanding influence beyond technical peers to senior business leaders.

    You're ready to move on when

    • Successfully mentored 3+ junior/mid-level analysts to significant growth.
    • Led a major cross-functional analytics initiative from conception to impact, demonstrating strong project ownership.
    • Consistently influenced product or engineering roadmaps with data-driven proposals.
    • Demonstrated ability to translate complex technical concepts into clear business implications for non-technical audiences.
  2. 2

    From Senior Analytics Engineer (L3) - Accelerated

    4-6 years at L3 (with exceptional leadership potential)

    Skills to master

    • Rapidly developing leadership and strategic planning skills, taking on informal leadership roles, and proactively seeking opportunities to manage projects and people. This path requires a strong focus on self-development and proactive initiative.

    You're ready to move on when

    • Actively sought out and successfully completed informal leadership opportunities (e.g., leading a guild, mentoring multiple peers).
    • Demonstrated exceptional communication and influence with senior stakeholders.
    • Identified and proposed solutions for organisational-level analytics challenges, not just technical ones.
    • Consistently delivered high-impact technical work while also developing junior team members.
  3. 3

    External Hire from Similar Managerial Role

    N/A (direct entry)

    Skills to master

    • Adapting to our specific technical stack, business domain, and company culture. Quickly building trust and credibility with a new team and senior stakeholders.

    You're ready to move on when

    • Proven track record of managing analytics teams of similar size and scope in a fast-paced technical environment.
    • Ability to articulate clear examples of driving business impact through analytics leadership.
    • Strong cultural fit and alignment with our values around collaboration, ownership, and continuous learning.
    • Demonstrable experience in navigating complex stakeholder environments and managing a significant budget.

16How people get here · where they go next

Came from
Lead/Staff Analytics Engineer
2-4 years
You mastered the art of translating complex technical data into strategic business insights, preparing you to lead a team.
You are here
Analytics Manager
Principal/Manager (12-16 years)
An Analytics Manager leads a team of analysts and sets the direction for how data is used to answer business questions. The role means turning big questions into analytics projects, then guiding the team to deliver them.
Goes to
Director of Analytics
3-5 years
This role involves managing multiple analytics teams, shaping the overarching data strategy, and influencing executive-level decisions.

The long view: From here, some Analytics Managers move into wider data leadership roles, such as Chief Data & Analytics Officer or Head of Data Science.

17Pay and demand

Pay & demand

Typical pay £55,519 a year Source: ONS Annual Survey of Hours and Earnings, 2025 (provisional), under the Open Government Licence v3.0

The figure is the median for full-time employees in the ONS occupation this job title codes to (Research and development (R&D) managers), from the April 2025 survey, about six months old when published, as ASHE always is. It is that occupation's middle, not this role's. Half earn more.

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 Analytics Manager 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 Analytics Manager.

Data Analytics Level 5

Applied to your work in Analytics Manager

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.

The Navigator Last time, we discussed aligning your analytics roadmap with the company's strategic objectives. How did that go in your recent meeting with the VP of Engineering?

You It went well, but I think there's room for improvement in how we present our data insights.

The Navigator Great insight! Let's focus on refining your presentation approach by incorporating more visual storytelling techniques in your next proposal.

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.

A year from now

A year from now, you see yourself as a pivotal figure in transforming data into strategic insights that shape the company's direction.

See Your Progress Grow Illustration
Analytics Manager
  • Experimentation & Causal Inference
  • Data Governance & Lineage
  • Product Analytics Frameworks
  • Predictive Modeling & Forecasting
  • Dimensional Modeling
  • Stakeholder-Centric Roadmapping
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.

18The 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

Analytics Manager is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Director of Analytics (L6)

    3-5 years as Analytics Manager

    From managing a functional team to managing multiple teams or a large business unit's analytics strategy, with a significantly larger budget and broader organisational influence.

    • Enterprise Data Strategy Defining the overarching data vision for a significant portion of the company.
    • Advanced Vendor Management & Negotiation Managing relationships and contracts with major data and analytics platform providers.
    • M&A Due Diligence (Data & Analytics) Assessing data capabilities and risks during mergers and acquisitions.
    • Cross-Functional Analytics Governance Establishing and enforcing data governance policies across multiple departments.
Working with AI on the job

Working with AI

Where AI is starting to help

As an Analytics Manager, your plate is always full. You're juggling team development, strategic planning, stakeholder management, and still trying to stay hands-on. What if you could reclaim a significant chunk of your week? AI isn't here to replace your strategic brain; it's here to amplify it, freeing you from the mundane so you can focus on what truly matters: leading your team and driving impact.

We're not just talking about basic chatbots. We're embedding AI into our daily workflows to automate routine tasks, accelerate insights, and streamline communication. For you, this means less time in the weeds and more time shaping the future. Here's how AI can transform your week:

Code Review & Generation Assistant

Imagine quickly reviewing your team's complex SQL or Python code for errors, optimisations, and best practice adherence, all with AI assistance. Or, use it to rapidly prototype new analytical scripts, allowing your team to focus on the 'why' rather than the 'how'. This means faster development cycles and fewer bugs, giving you more confidence in your team's output.

Automated Insight Discovery & Summarisation

Instead of manually digging through dashboards, use AI-powered tools to automatically flag anomalies, identify key drivers of change, and summarise complex findings. You'll get executive-ready insights in minutes, allowing you to quickly grasp the 'so what' and guide your team's next steps, rather than spending hours sifting through raw data.

Strategic Research & Competitive Analysis

Need to quickly understand the latest trends in product analytics frameworks or evaluate a new data warehousing technology? AI can rapidly summarise academic papers, vendor documentation, and competitive analyses, providing you with concise, actionable intelligence to inform your strategic decisions and keep your team ahead of the curve.

Executive Communication & Roadmap Drafting

Drafting quarterly business reviews, strategic proposals, or detailed team roadmaps can be a time sink. Use AI to generate first drafts of executive summaries, translate technical jargon into clear business language, and structure compelling presentations. This ensures your strategic vision is articulated clearly and persuasively, saving you hours of writing and refining.

Common questions

Common questions

How do you become an Analytics Manager?

Common routes in include From Lead/Staff Analytics Engineer (2-4 years at L4), From Senior Analytics Engineer - Accelerated (4-6 years at L3 (with exceptional leadership potential)) and External Hire from Similar Managerial Role (N/A (direct entry)). Times vary with prior experience.

Where can an Analytics Manager progress to?

This role can lead on to Director of Analytics (3-5 years as Analytics Manager), depending on the skills you build.

What new skills matter most for an Analytics Manager?

Increasingly, Prompt Engineering & LLM Integration for Analytics Workflows and Data Product Management & Monetisation. 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

19Where 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 involved, leading technical teams, shaping data strategy, and influencing decisions at senior level, apply across many sectors. This can lead to similar leadership roles in fields such as FinTech, HealthTech or SaaS, or into product leadership and operational strategy roles where data drives decisions.

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.