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

Analytics Manager

As an Analytics Manager, you turn complex data into the strategic insights that guide our engineering and product teams.

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.

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Free to start: a plan built for this role, and a Future Fluency module to try first. No card.

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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 reports3-8 reports
Reports toDirector of Analytics
UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Head of Analytics (Technical) · Lead Data Manager · Principal Analytics Lead

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

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 4 of 12 standards

Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 2 of 12 standards

NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 8 of 12 standards

OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 6 of 12 standards

Pearson BTEC Level 4 Higher National Certificate in Computing for England Pearson Education Ltd · Level 4 · covers 3 of 12 standards

BCS Level 4 Diploma - Digital Modular Programme for Data Analysts BCS, The Chartered Institute for IT · Level 4 · covers 2 of 12 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

Automation Fluency building AI into how work runs

Systems Fluency seeing the whole picture

Start my Analytics Manager plan, free

Your Plan Illustration

Built for Analytics Manager

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

Data Analytics Pearson Education Ltd · covers 4 of 12 standards Level 5
Data Analytics Primer NOCN · covers 8 of 12 standards Level 4
Data Analysis and Visualisation OTHM Qualifications · covers 6 of 12 standards Level 7
Data Analysis BCS, The Chartered Institute for IT · covers 2 of 12 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 taking over the repetitive data cleaning and basic reporting tasks, freeing you to focus on strategy and insights.

→

Rising: worth more because of AI

Your ability to interpret AI-generated data and provide nuanced insights becomes even more valuable.

The new skill this role is being asked for

AI-Driven Workflow Orchestration

Why each one is rising

The future skills this job leans on

AI Fluencyworking with AI as a thinking partner
Data Fluencyturning numbers into a decision
Automation Fluencybuilding AI into how work runs
Systems Fluencyseeing the whole picture

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 a Future Fluency module on using AI in your job. No card.

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

Start my Analytics Manager plan, free Ready to pay? Subscribe for £70 a month

3What this role really is

We see you

You sometimes wonder if AI will make your role redundant, but you know your knack for storytelling with data is irreplaceable. It's about finding the balance between technology and the human touch in decision-making.

An Analytics Manager leads a small team of analysts who examine technical operations, product performance and engineering efficiency.

4A day in the life

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

08:45

You start your day with a quick check-in with your team, ensuring everyone is aligned and ready to tackle their tasks.

11:00

You meet with the Product VP to discuss high-level business questions and translate them into an actionable analytics roadmap.

14:30

You review a new FinOps dashboard, ensuring the data is accurate and the insights are clear before giving your final sign-off.

16:15

You spend time mentoring a junior analyst, guiding them through a complex dataset and helping them build their analytical skills.

5What you'd actually use

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

SQL (PostgreSQL, Snowflake SQL) Strategic

Setting standards for query performance and cost management across the team; reviewing complex queries; guiding architectural implications of SQL patterns.

Python (pandas, NumPy, scikit-learn) Strategic

Evaluating the ROI of building custom Python models vs. using off-the-shelf tools; setting coding standards and review processes for the team; guiding model productionisation.

BI & Visualisation (Looker, Tableau) Architect

Leading BI platform strategy; designing the enterprise-wide data model architecture (e.g., LookML project structure); defining and enforcing governance policies for dashboards.

Product Analytics (Amplitude, Mixpanel) Strategic

Integrating product analytics data with other sources (CRM, financial) for holistic customer views; guiding event tracking schema design; identifying strategic user behaviour patterns.

Data Platform (Snowflake, Databricks) Architect

Owning vendor relationships; making strategic decisions on data warehousing, data lakes, and compute architecture; managing the platform budget and cost optimisation.

Project & Doc Mgmt (Jira, Confluence) Strategic

Using Jira data to forecast team capacity and report on project velocity; championing a culture of documentation; setting and enforcing team documentation standards.

Executive & Governance (Anaplan, Collibra) Strategic/Architect

Managing data models in Anaplan for headcount and budget planning; defining and enforcing data policies, lineage, and definitions in Collibra across the technical domain.

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 choice	Coming in	Where you are now	The step above
Team Project Prioritisation	Follows assigned tasks; escalates any conflicts to manager.	Prioritises own tasks within project scope; consults manager on conflicts.	Prioritises workstreams within a project; makes recommendations for project-level prioritisation to manager.
Technical Methodology & Tool Selection	Uses established tools and methods; seeks guidance for new approaches.	Selects appropriate tools/methods for routine analyses; proposes new tools for manager review.	Designs and implements new technical methodologies; makes recommendations for tool adoption within workstream scope.
Budget Allocation (Team)	No budget authority.	No budget authority.	No budget authority.
Hiring & Performance Management	No authority.	No authority.	Provides input on junior hires; mentors.

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.

Team Project Delivery Rate

Percentage of analytics projects completed on time and within scope by your team.

Target · 90% of projects delivered by agreed deadline

If your team commits to 10 projects in a quarter, 9 of them should be finished and signed off by the original target date.

Insight-to-Action Conversion

The proportion of your team's key insights that lead to a documented change in product roadmap, engineering process, or operational strategy.

Target · >25% of strategic insights lead to demonstrable action

Your team identifies a 15% cost saving opportunity in cloud spend; Engineering then implements changes based on this analysis, resulting in actual savings.

Data Literacy Improvement (Stakeholders)

Increase in key stakeholders' ability to interpret and use data effectively, measured through feedback and reduced 'report monkey' requests.

Target · 15% increase in stakeholder data literacy score year-over-year

After a year, the VP of Engineering can confidently use a Looker dashboard to answer their own questions, reducing direct requests to your team by 20%.

Team Attrition Rate

The percentage of your direct reports who leave the company within a given period.

Target · <10% annual attrition rate for your direct reports

If you manage 5 analysts, you'd ideally see no more than 1 person leave over a two-year period, indicating a healthy team environment.

Self-Service Adoption Rate

The proportion of recurring data questions that stakeholders can answer themselves using existing dashboards and tools, without needing an analyst.

Target · >50% of recurring questions answered via self-service

Instead of asking your team for the monthly active user count, Product Managers are pulling it directly from the Looker dashboard 60% of the time.

Team Morale & Development

How well your team feels supported, challenged, and developed under your leadership.

Positive feedback in 1-on-1s and skip-level meetings
team members taking on new responsibilities
successful internal promotions
low team stress levels even during busy periods.

Strategic Influence

Your ability to shape the technical roadmap and business strategy through data-driven recommendations.

Being proactively invited to strategic planning meetings by VPs
your team's insights directly informing major product or engineering decisions
stakeholders seeking your opinion on critical initiatives before data is even available.

Quality of Analytical Output

The robustness, accuracy, and clarity of your team's analyses and dashboards.

Minimal errors or corrections needed post-delivery
analyses are easy for non-technical stakeholders to understand
models are well-documented and reproducible
high praise from stakeholders for the depth and clarity of insights.

Cross-Functional Collaboration

How effectively your team works with Product, Engineering, and other departments to achieve shared goals.

Positive feedback from other department leads about your team's responsiveness and helpfulness
joint projects delivered successfully
your team being seen as a trusted partner, not just a service provider
proactive identification of data needs from other teams.

8Would you like it

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

What people enjoy

Solving Complex Technical Puzzles

You'll be leading your team through intricate data challenges, like figuring out why a specific microservice is causing latency spikes or optimising our cloud spend by attributing costs to individual features. It's about unravelling technical mysteries with data.

Your team identifies a subtle bug in our event tracking that was skewing conversion metrics for a key product feature, and you lead the effort to diagnose and fix it.

Building and Developing a High-Performing Team

A big part of your day will be coaching your analysts, helping them refine their SQL, improve their Python skills, and get better at presenting their findings. You'll see them grow under your guidance and take on bigger challenges.

One of your junior analysts, after a year of your mentorship, successfully leads a complex A/B test analysis and presents the findings to senior leadership.

Driving Tangible Business Impact through Data

You won't just be producing reports; you'll be seeing your team's analyses directly influence significant decisions—like a £500K budget reallocation for infrastructure or a major pivot in the product roadmap. Your work has real consequences.

Your team's FinOps analysis leads to a 10% reduction in our quarterly AWS bill, directly impacting the company's profitability.

What frustrates people

Upstream Data Chaos: Engineering changes a logging format or an API response without warning, breaking all dependent pipelines and dashboards. Your team is often the first to know when everything is on fire.
The 'Report Monkey' Syndrome: Despite being a manager, your team might still be treated as a service desk for pulling data rather than a strategic partner who can define what should be measured.
Fighting for Instrumentation: Constantly having to justify to Product Managers why engineering time should be spent on adding proper tracking and logging instead of shipping the next shiny feature.
The Politically Inconvenient Truth: Presenting data that shows a pet project is failing, and then having to navigate the political fallout with its executive sponsor.
Misinterpreted Data: Your team spends a week creating a nuanced analysis, only to see a stakeholder screenshot one chart out of context to 'prove' a point you didn't make.

What this role does not give you

A perfectly clean data environment from day one.
Complete control over all priorities and project timelines.
A guarantee that every analysis your team produces will be acted upon.
A purely individual contributor path; you'll be leading and developing others.

9Who you work with

This role directly shapes how our technical teams use data to improve. You're not just reporting numbers; you're building the capability that allows us to understand product usage, engineering performance, and infrastructure costs. Your team's work will directly influence resource allocation, product roadmap prioritisation, and operational efficiency, potentially saving the company millions in cloud spend or accelerating product delivery.

Inside the business

VP of Engineering
Chief Product Officer (CPO)
Head of Infrastructure
Finance Business Partners
Other Analytics Managers

Outside the business

Cloud Service Providers (e.g., AWS, Azure)
Data Platform Vendors
Industry Peers (for benchmarking)

10What you need before you start

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

Proven experience (at least 5-8 years) as a Senior Technical Analyst or similar role, where you've owned complex analytical projects end-to-end.
Demonstrable experience mentoring junior analysts, including code reviews, technical guidance, and career development conversations.
Expert-level SQL skills, capable of writing and optimising highly complex queries for large datasets.
Advanced proficiency in Python (pandas, NumPy) for data manipulation, statistical analysis, and scripting.
Extensive experience with at least one major BI tool (Looker, Tableau) for building and maintaining complex dashboards and data models.
A solid understanding of statistical methods for experimentation (A/B testing) and interpreting results.
Experience working directly with engineering and product teams, translating technical data into business insights.

11What to practise next

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

Data Mesh & Data Product Management

As our data ecosystem grows, the traditional centralised data warehouse model struggles to scale. Data Mesh is gaining traction, treating data as a product owned by domain teams. You'll need to guide your team in this decentralised world.

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

This quarter: Read Zhamak Dehghani's 'Data Mesh' book and discuss key concepts with your team.
Next quarter: Identify one 'data product' within your domain (e.g., 'user activity stream') and define its consumers, SLAs, and ownership.
Month 4-6: Work with Data Platform and Engineering to prototype a 'data product' interface for one of your team's key datasets.
Ongoing: Advocate for data mesh principles in our internal data strategy discussions.

Quick win: Start by improving the 'product-ness' of your team's existing dashboards and data models: clearer documentation, defined owners, and explicit SLAs for data freshness.

Advanced Observability for Technical Systems

Beyond basic monitoring, true observability means understanding the internal state of a system from its external outputs. As systems become more distributed and complex, your team needs to use advanced techniques to diagnose issues and understand performance.

Distributed Tracing · Structured Logging & Metrics · Service Mesh Analytics · AIOps for Anomaly Detection

This month: Deep dive into our current observability stack (e.g., Datadog, Grafana) and identify gaps.
Next quarter: Work with an Engineering Manager to define a new 'observability metric' that provides deeper insight into a critical service.
Month 4-6: Lead your team in building a dashboard that combines distributed tracing data with traditional metrics to provide a holistic view of system health.
Ongoing: Research new observability tools and techniques, and propose pilots for relevant ones.

Quick win: Ensure all new services or features have comprehensive, structured logging from day one. It's a small change that pays dividends later.

12Staying current once you are in

What people here do to keep up

Regularly attend industry conferences (e.g., ODSC, Data + AI Summit, FinOps X) to stay current on trends and network with peers.
Contribute to open-source data projects or maintain a personal portfolio of analytical work.
Actively participate in online communities (e.g., dbt Slack, Data Engineering Weekly) to learn from and share with other professionals.
Take advanced courses in machine learning engineering, cloud architecture, or leadership development.
Seek out mentorship from more senior analytics leaders within the company or externally.

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.

AI-Driven Workflow Orchestration

AI isn't just for generating code anymore; it's becoming the glue that connects different tools and automates entire analytical workflows. Competitors are already using AI to chain together data extraction, cleaning, analysis, and reporting, massively accelerating their time to insight.

No-code/Low-code AI Platforms
Agentic AI Systems
Human-in-the-Loop Validation
API Integration for AI Tools

14What you’ll use

What you’ll use

Skills this role draws on

Technical

Experimentation & A/B Testing Design
Product Funnel & User Journey Analysis
Data Modelling (Kimball/Star Schema)
Engineering Performance Frameworks (DORA, SPACE)
Cloud Cost Intelligence (FinOps)
Data Governance & Lineage

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
Senior Technical Analyst (L3) to Analytics Manager (L5)
2-4 years as a Senior Technical Analyst
Skills to master
- Leading complex projects, mentoring junior team members, developing strong stakeholder relationships, translating business strategy into analytical roadmaps, and demonstrating leadership potential.
You're ready to move on when
- Successfully led 3+ cross-functional analytics projects with significant business impact.
- Consistently received positive feedback on mentorship and guidance of junior colleagues.
- Proactively identified and championed new analytical approaches or tools.
- Demonstrated ability to manage project timelines and stakeholder expectations independently.
2
Staff/Lead Analyst (L4) to Analytics Manager (L5)
1-2 years as a Staff/Lead Analyst
Skills to master
- Expanding technical depth into broader architectural decisions, defining technical standards, influencing strategic direction without direct authority, and preparing for people management responsibilities.
You're ready to move on when
- Architected and implemented major data solutions or analytical frameworks.
- Recognised as a go-to expert for a specific technical domain (e.g., FinOps, Product Analytics).
- Actively contributed to hiring and onboarding processes.
- Demonstrated ability to influence senior technical and business stakeholders.
3
Analytics Manager from a different technical domain (external hire)
N/A (direct entry)
Skills to master
- Quickly understanding our specific technical stack, product landscape, and organisational dynamics. Building credibility with engineering and product leadership.
You're ready to move on when
- Proven track record of managing technical analytics teams in similar fast-paced environments.
- Strong foundational technical skills directly transferable to our stack.
- Demonstrated ability to onboard quickly and make an impact within the first 6 months.
- Excellent communication and stakeholder management skills to adapt to a new environment.

16How people get here · where they go next

Came from

Senior Technical Analyst (L3)

2-4 years

You mastered the art of leading complex projects and mentoring junior team members, preparing you to step up.

You are here

Analytics Manager

Principal/Manager (12-16 years)

An Analytics Manager leads a small team of analysts who examine technical operations, product performance and engineering efficiency.

Goes to

Director of Analytics (L6)

3-5 years

This role involves managing multiple analytics teams and shaping the department's strategic direction.

The long view: This role can lead to broader data leadership positions, or into director-level roles such as Director or Principal Architect.

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.

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 talked about aligning your analytics roadmap with the VP's strategic goals.

You Yes, I've been thinking about how to better integrate our data insights with their priorities.

The Navigator Let's focus on identifying one key project where your team's insights can directly influence those goals, and plan how you'll present your findings to the VPs.

How the thinking builds

1 Remember
2 Understand
3 Apply
4 Analyse
5 Evaluate
6 Create

An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

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

Start my Analytics Manager plan, free

A year from now

A year from now, you are a confident leader, seamlessly integrating AI insights into your team's workflow and driving strategic decisions with clarity.

See Your Progress Grow Illustration

Analytics Manager

Experimentation & A/B Testing Design
Product Funnel & User Journey Analysis
Data Modelling (Kimball/Star Schema)
Engineering Performance Frameworks (DORA, SPACE)
Cloud Cost Intelligence (FinOps)
Data Governance & Lineage

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.

Director of Analytics (L6)
3-5 years as an Analytics Manager

Managing multiple teams or a larger, critical analytics function. Owning significant budget and technology strategy for an entire analytics area.
- Enterprise Data Strategy Developing and implementing a holistic data strategy across multiple domains and business units.
- Vendor Management (Strategic) Managing relationships with key data and analytics vendors, negotiating contracts, and evaluating new technologies.
- Budget & P&L Management Owning and managing a substantial departmental budget, optimising spend, and reporting on financial performance.
Principal/Staff Analytics Architect (L5/L6 IC Path)
3-5 years as an Analytics Manager (or from Staff/Lead Analyst)

Deepening technical expertise to become a recognised company-wide authority on data architecture, advanced modelling, or a specific technical domain. This is an Individual Contributor path with comparable impact and compensation.
- Advanced Data Architecture Design Designing scalable, resilient, and cost-effective data solutions for enterprise-wide use cases.
- Machine Learning Engineering Moving beyond analytical models to building and deploying production-grade ML systems for business problems.
- Data Security & Compliance (Deep Dive) Becoming the go-to expert on data security best practices and regulatory compliance for complex data systems.

Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, managing an analytics team means you're always looking for ways to do more with less. The good news? AI isn't just a buzzword; it's a practical toolkit that can seriously boost your team's productivity and free them up for more strategic work. We're talking about automating the tedious bits so your brilliant analysts can focus on the 'aha!' moments.

In technical analytics, AI can take over a surprising amount of the grunt work. From drafting complex SQL queries to spotting anomalies before they become problems, these tools are already changing how we work. As an Analytics Manager, you'll be championing these tools, helping your team integrate them into their daily workflow, and ultimately making everyone more effective.

SQL Query Generation & Optimisation

Use AI assistants (think GitHub Copilot for SQL) to translate natural language requests like 'Show me user growth by country for the last 6 months, excluding internal IPs' into complex, optimised SQL queries. It's a massive time-saver for drafting initial queries or debugging tricky ones.

Automated Anomaly Detection & Root Cause Analysis

Deploy AI-powered monitoring tools on your key technical metrics (API error rates, server load, conversion funnels). The AI flags statistically significant anomalies automatically and even suggests potential correlated events, helping your team pinpoint issues much faster than manual digging.

Documentation & Code Commenting

Leverage AI tools to automatically generate clear, concise documentation for new data models in Confluence or add descriptive comments to complex Python scripts and SQL queries. This dramatically improves maintainability, knowledge sharing, and onboarding for new team members.

Executive Summary & Narrative Generation

After your team builds a complex dashboard or analysis, feed the key charts and data points to an AI model. Ask it to draft a high-level executive summary or a first pass at a presentation narrative. This gives your analysts a strong starting point for stakeholder communications, saving hours on drafting.

Common questions

How do you become an Analytics Manager?

Common routes in include Senior Technical Analyst to Analytics Manager (2-4 years as a Senior Technical Analyst), Staff/Lead Analyst to Analytics Manager (1-2 years as a Staff/Lead Analyst) and Analytics Manager from a different technical domain (external hire) (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 an Analytics Manager) and Principal/Staff Analytics Architect (3-5 years as an Analytics Manager (or from Staff/Lead Analyst)), depending on the skills you build.

What new skills matter most for an Analytics Manager?

Increasingly, AI-Driven Workflow Orchestration. These are the skills our record of this role lists as growing.

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 Analytics Manager plan, free What happens next: a free account, then we build your plan as you set up. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.

Analytics Manager

Where should we send it?

Two short quizzes about you

Will AI change your work as an Analytics Manager?

What's rising in its place

AI-Driven Workflow Orchestration

Skills this role draws on

Technical

How you actually get there, here

Senior Technical Analyst (L3) to Analytics Manager (L5)

Staff/Lead Analyst (L4) to Analytics Manager (L5)