United Kingdom · Technology and data · Mid-Level (2-5 years)

Analytics Manager

As an Analytics Manager, you transform complex data into the stories that guide our technical teams' next moves.

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.

Start my Analytics Manager plan, free

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

Keep this job description

Where should we send it?

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

Upskilling a team in this role? Talk to us

Experience bandMid-Level (2-5 years)
Direct reportsNo direct reports
Reports toSenior Analytics Manager
UK framework levelUsually a coordinator, or early in a professional job

Also advertised as Technical Analyst · Data Analyst (Technical) · Product Analytics Specialist

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

NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 4 of 7 standards

BCS Level 4 Diploma - Digital Modular Programme for Data Analysts BCS, The Chartered Institute for IT · Level 4 · covers 2 of 7 standards

Highfield Level 3 Diploma for Team Leaders and Supervisors (RQF) Highfield Qualifications · Level 3 · covers 2 of 7 standards

Pearson BTEC Level 4 Higher National Certificate in Computing for England Pearson Education Ltd · Level 4 · covers 2 of 7 standards

Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 3 of 7 standards

Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 1 of 7 standards

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Data Analytics Primer NOCN Level 4 certificate for Data Analysts

Data Analysis BCS Level 4 Diploma - Digital Modular Programme for Data Analysts

Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England

The future skills this job leans on

AI Fluency working with AI as a thinking partner

Data Fluency turning numbers into a decision

Systems Fluency seeing the whole picture

Start my Analytics Manager plan, free

Your Plan Illustration

Built for Analytics Manager

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

Data Analytics Primer NOCN · covers 4 of 7 standards Level 4
Data Analysis BCS, The Chartered Institute for IT · covers 2 of 7 standards Level 4
Data Analytics Pearson Education Ltd · covers 3 of 7 standards Level 5

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 routine data cleaning and initial report drafting, freeing you from the busywork.

→

Rising: worth more because of AI

Your ability to interpret nuanced data stories and apply them to strategic decisions becomes even more valuable.

The new skill this role is being asked for

Prompt Engineering & LLM Integration

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
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 replace the heart of your work, but deep down, you know that human insight still reigns supreme. The numbers may speak, but it's your understanding that makes them sing.

An Analytics Manager makes sense of data and helps product and engineering teams make smarter decisions. The role involves understanding how products are used and where engineering effort pays off. It means digging into data, spotting trends, and turning findings into clear insights for product and engineering teams. It covers the whole analysis, from messy data to a clear story.

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 by reviewing yesterday's dashboards, ensuring all data points are accurate and ready for the morning's team meeting.

11:00

A Product Manager drops by virtually with a question about a sudden drop in user engagement; you dive into the data to uncover the story behind the numbers.

14:30

You spend the afternoon designing a new A/B test for an upcoming feature, calculating sample sizes and setting the parameters for a robust analysis.

16:15

Wrapping up the day, you document your findings and methodologies on Confluence, knowing future-you will appreciate the clarity.

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) Intermediate

Writing complex joins, CTEs, and window functions to answer defined business questions. Debugging and optimising your own queries to get the data you need efficiently.

Python (pandas, NumPy) Basic

Performing data cleaning, manipulation, and simple statistical analysis in Jupyter notebooks. Following existing code patterns for data processing and analysis.

BI & Visualization (Looker, Tableau) Intermediate

Building and maintaining dashboards from existing data models. Answering ad-hoc questions using the BI tool's interface and creating clear, interactive visualisations.

Product Analytics (Amplitude, Mixpanel) Intermediate

Building funnels, retention charts, and user segments to answer specific questions from Product Managers about user behaviour.

Data Platform (Snowflake, Databricks) Basic

Understanding the basic architecture, loading data from S3, and querying tables efficiently. Being aware of compute vs. storage concepts.

Project & Doc Mgmt (Jira, Confluence) Intermediate

Managing your own tasks in Jira, documenting analysis and findings clearly in Confluence, and collaborating on project plans.

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
Analytical Methodology & Tool Selection	Follows prescribed methods; uses assigned tools.	Chooses appropriate methods and tools for routine problems; consults on novel approaches.	Defines and champions best practices; evaluates and recommends new tools/methods for the team.
Data Definition & Metric Creation	Uses existing definitions; flags discrepancies.	Proposes new metric definitions for specific analyses; ensures consistency within owned dashboards.	Establishes and governs official metric definitions across multiple domains; resolves conflicts.
Project Prioritisation (within own workload)	Executes tasks as assigned by supervisor.	Manages own task queue based on agreed priorities; escalates conflicts to manager.	Helps define team priorities; negotiates with stakeholders on timelines and scope.
Stakeholder Communication & Recommendations	Presents findings to supervisor or immediate team.	Presents findings and recommendations directly to Product/Engineering peers and leads.	Presents strategic insights and recommendations to senior leadership (Director/VP level).

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.

Ad-hoc Query Turnaround Time

The average time it takes to deliver a complete, validated analysis for an ad-hoc request.

Target · 90% of requests completed within 48 hours (excluding major deep dives).

A Product Manager asks 'How many users dropped off at step 3 of the new onboarding flow last week?' You deliver a clear answer with supporting data within 24 hours.

Dashboard Accuracy & Reliability

The percentage of dashboards and reports you own that are free from data errors or breaks.

Target · >99% accuracy; <1% downtime/errors on owned dashboards.

Your 'Engineering Performance' dashboard consistently shows correct DORA metrics, and no one has reported a data discrepancy in months.

Insight-to-Action Rate (Self-Initiated)

The percentage of your proactive analyses (not direct requests) that lead to a documented change or decision by a stakeholder.

Target · >15% of proactive insights lead to action.

You spot a recurring pattern of high cloud spend in a specific service, flag it to the Engineering Lead, and they initiate a project to optimise it based on your data.

Experimentation Analysis Quality

The accuracy and statistical rigour of your A/B test analyses and recommendations.

Target · No statistical errors identified in post-review; clear, unbiased recommendations.

You correctly identify a statistically significant uplift (or no uplift) from a new feature, and your report clearly explains the confidence intervals and potential confounding factors.

Stakeholder Trust & Partnership

How much Product and Engineering teams see you as a trusted partner, not just a data provider.

You're proactively included in early planning meetings for new features or engineering initiatives. Stakeholders come to you for advice on what to measure, not just how to measure it. They value your opinion on 'is this a one-way or two-way door decision?'.

Clarity of Communication

Your ability to translate complex technical analysis into clear, concise, and actionable language for non-technical audiences.

Your presentations are easy to understand, even for executives. Product Managers can take your findings directly to their teams without needing further explanation. You receive feedback like 'that was really clear' or 'I finally understand that metric'.

Analytical Rigour & Methodical Approach

Your commitment to thorough, reproducible analysis, even when under pressure.

You consistently document your methodology, assumptions, and data sources. You're known for asking the right 'why' questions before diving into the data. You catch data quality issues early and flag them, rather than just running with potentially flawed numbers. You always 'check the data lineage'.

Proactive Problem Identification

Your ability to spot potential issues or opportunities in the data before anyone asks you to look.

You regularly bring new insights or concerns to your manager or stakeholders without being prompted. You're often the first to notice when 'the pipeline broke' or a key metric is behaving unusually. You're genuinely 'deeply curious' about how things work.

8Would you like it

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

What people enjoy

Impact & Influence

You'll feel a real buzz when your analysis directly leads to a product change, an engineering optimisation, or a significant cost saving. Seeing your data drive a decision is what gets you going.

Presenting your findings on a specific user drop-off point, and then seeing the Product team prioritise a fix based on your recommendation.

Problem Solving & Discovery

You love the challenge of taking a really vague question and breaking it down into a solvable data problem. You enjoy the 'aha!' moment when you uncover a hidden trend or an unexpected insight in the data.

Being given a broad question like 'Are users happy with the new feature?' and then designing the metrics, pulling the data, and finding out exactly why some are and some aren't.

Technical Mastery & Learning

You're keen to deepen your skills in SQL, Python, and our BI tools. You enjoy learning new analytical techniques and applying them to real-world business problems. You're always looking for a better, more efficient way to do things.

Spending time researching a new Python library for time-series analysis and then implementing it to improve a forecasting model.

What frustrates people

The 'Data Janitor Job': Spending 60% of your time cleaning, de-duping, and joining messy, undocumented datasets.
Fighting for Instrumentation: Constantly having to justify why engineering time should be spent on proper tracking instead of shipping the next shiny feature.
Misinterpreted Data: You spend a week on a nuanced analysis, only for a stakeholder to screenshot one chart out of context to 'prove' a point you didn't make.
The 'urgent' request that disrupts your entire sprint, only to be deprioritised a day later.

What this role does not give you

A perfectly clean, well-documented data environment (you'll help build it, but it's not there yet).
A purely strategic role where you never have to write SQL or clean data.
Guaranteed deployment of every model or analysis you build (sometimes the business moves on).

9Who you work with

Your work directly impacts the strategic direction of our product and engineering efforts. By providing clear, unbiased data, you help prevent costly mistakes, identify new opportunities, and ensure our technical resources are allocated where they'll have the biggest impact. Getting it right means better products and a healthier bottom line. Getting it wrong means wasted effort and missed opportunities.

Inside the business

Product Managers (for feature analysis, A/B testing)
Engineering Leads (for performance metrics, system health)
Finance Business Partners (for cloud cost intelligence)
Data Engineering Team (for data quality and pipeline issues)
Senior Analytics Manager (for guidance and project alignment)

Outside the business

N/A (primarily internal-facing role)

10What you need before you start

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

At least 2 years of hands-on experience in a data analysis or technical analytics role, ideally within a technical product company.
Demonstrable experience writing complex SQL queries for data extraction and transformation.
Proven ability to build and maintain dashboards in a major BI tool like Looker or Tableau.
Experience in designing and analysing A/B tests, including understanding statistical significance.
A portfolio or examples of previous analytical work (e.g., GitHub repo, blog posts, project summaries) that showcase your problem-solving skills.

11What to practise next

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

Advanced Data Modelling & Governance

As our data grows, so does the complexity. You'll need to move beyond just using existing models to contributing to their design and ensuring their integrity. This is about building trust in our data at scale.

Data quality monitoring frameworks · Metadata management and data cataloguing · Data lineage tools (e.g., Collibra) · Data contract design

This quarter: Take an online course on advanced data modelling or data governance principles.
Next quarter: Actively participate in data quality discussions with Data Engineering.
Within 6 months: Propose an improvement to an existing data model or documentation standard.
Within 9 months: Start using Collibra (or similar) to document lineage for your key metrics.

Quick win: Start proactively documenting the lineage of your most critical metrics in Confluence right now.

Cloud Cost Optimisation & FinOps Principles

Cloud spend is a massive cost for technical companies. As we scale, understanding and optimising these costs becomes critical. You'll need to move from basic awareness to actively identifying and recommending cost-saving opportunities.

Reserved Instances & Savings Plans · Cost attribution and chargeback models · Waste identification (idle resources, over-provisioning) · Cost anomaly detection

This quarter: Get more familiar with our cloud provider's billing console and cost explorer.
Next quarter: Work with a specific Engineering team to analyse their cloud spend and identify one optimisation opportunity.
Within 6 months: Present a summary of cloud cost drivers and potential savings to your manager.
Within 9 months: Take a basic FinOps certification or course.

Quick win: Regularly review the AWS CUR (Cost and Usage Report) or equivalent for unusual patterns in your spare time.

12Staying current once you are in

What people here do to keep up

Actively participate in online data communities (e.g., Analytics Engineering Collective, local meetups) to stay current with industry trends and best practices.
Take advanced courses on statistical analysis, experimental design, or machine learning fundamentals to deepen your analytical toolkit.
Read books and articles on data storytelling and effective communication to improve your ability to influence decisions.
Contribute to open-source projects or build personal data projects to continuously hone your technical skills and explore new technologies.

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

Honestly, competitors are already using tools like ChatGPT and Claude to draft reports in minutes that used to take hours. Analysts who figure this out will outproduce their peers significantly. It's not future-state; it's happening now.

Context windows and token limits
Temperature settings for different tasks
RAG architectures for proprietary data
Output validation and hallucination detection
Prompt chaining for complex analysis

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 Modeling (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
Junior Data Analyst / Associate Technical Analyst
2-3 years
Skills to master
- Mastering SQL, building basic dashboards, understanding data lineage, and effectively communicating simple findings. Getting really good at 'GIGO' prevention.
You're ready to move on when
- Consistently delivering accurate and timely analyses for well-defined requests.
- Proactively identifying and flagging data quality issues.
- Building robust, easy-to-understand dashboards from existing data models.
- Demonstrating a strong grasp of our core technical data stack.
2
Product Analyst (entry-level)
2-4 years
Skills to master
- Deep understanding of product metrics, A/B testing principles, user journey analysis, and translating product questions into data queries. Becoming the go-to person for 'what's the instrumentation for this?'
You're ready to move on when
- Successfully supporting multiple product teams with data-driven insights.
- Designing and analysing simple A/B tests with clear recommendations.
- Mapping and measuring key user funnels and identifying drop-off points.
- Building strong relationships with Product Managers.
3
Data Scientist (entry-level, with strong analytics focus)
2-3 years
Skills to master
- Strong statistical foundations, basic machine learning concepts, Python for data manipulation and modelling, and applying scientific methods to business problems. Moving beyond descriptive to predictive analytics.
You're ready to move on when
- Building and validating simple predictive models (e.g., churn prediction).
- Demonstrating strong Python scripting skills for data analysis.
- Applying statistical rigour to complex analytical problems.
- Clearly articulating model assumptions and limitations.

16How people get here · where they go next

Came from

Junior Data Analyst / Associate Technical Analyst

2-3 years

You mastered the art of delivering accurate analyses and building intuitive dashboards that made data accessible to everyone.

You are here

Analytics Manager

Mid-Level (2-5 years)

Goes to

Senior Analytics Manager (L3)

2-3 years

You lead complex analytics projects, mentoring others and designing data models that drive significant business outcomes.

The long view: This role can lead to managing larger analytics teams as a Director of Analytics, or deepening technical expertise as a Principal Analytics Engineer.

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 Primer, worked against Analytics Manager.

Data Analytics Primer Level 4

Applied to your work in Analytics Manager

This unit aims to equip learners with a foundational understanding of data analytics, including its applications and the stages of the data analysis lifecycle. Learners will explore various data types and structures, understand the role of data within an organisation, and recognise the importance of GDPR and compliance requirements in data handling.

The Coach Last time, we talked about improving the clarity of your data visualisations for the engineering team.

You Yes, I've been trying to make the charts more intuitive.

The Coach Great! Let's focus on your next dashboard and see how we can enhance the storytelling aspect to make those insights pop.

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're the trusted voice in the room, effortlessly bridging the gap between raw data and strategic action.

See Your Progress Grow Illustration

Analytics Manager

Experimentation & A/B Testing Design
Product Funnel & User Journey Analysis
Data Modeling (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.

Senior Analytics Manager (L3)
2-3 years

You'll move from owning individual analyses to leading complex, cross-functional analytics projects. You'll also start mentoring junior analysts and designing new data models and tracking schemas. This is about becoming a trusted expert and a leader within the team.
- Designing and implementing new data models (e.g., LookML models).
- Leading the design of new event tracking schemas with Engineering.
- Advanced statistical methods for complex experimentation.
- Deep expertise in a specific domain (e.g., FinOps, Product Growth).
Staff/Lead Analyst (L4 - Individual Contributor path)
3-5 years

This path focuses on becoming a deep technical expert, solving the most complex data challenges and setting technical standards. You'll be the architect of analytical solutions, without necessarily managing people. It's about becoming the 'go-to expert' for a major domain.
- Architecting enterprise-level data models and analytical solutions.
- Evaluating and recommending new analytical technologies and platforms.
- Deep expertise in performance optimisation for large-scale queries and dashboards.
- Mentoring multiple junior and mid-level analysts on advanced technical topics.

Working with AI on the job

Working with AI

Where AI is starting to help

Let's be honest, a big chunk of an analyst's time is spent on repetitive tasks. Imagine getting that time back. Our team is embracing AI tools to automate the mundane, letting you focus on the really interesting, impactful work.

As an Analytics Manager (L2), you'll use AI to streamline everything from writing complex SQL queries to drafting executive summaries. This isn't about replacing your job; it's about making you significantly more productive and freeing you up for deeper, more strategic thinking. We're building an 'AI Productivity Hub' to help you get started quickly.

SQL Query Generation

Use AI assistants (like GitHub Copilot or a ChatGPT-powered tool) to translate natural language questions like 'Show me user growth by country for the last 6 months' into complex SQL queries, including joins and window functions. This means less time writing boilerplate and more time validating the logic.

Anomaly Detection & Root Cause Analysis

Deploy AI-powered monitoring tools on your key time-series metrics (e.g., API error rates, user activity). The AI will automatically flag statistically significant anomalies and even suggest potential correlated events that could be the root cause. You'll be the first to know when something's off, and you'll have a head start on finding out why.

Documentation & Code Commenting

Use AI tools to automatically generate clear documentation for your data models in Confluence or add descriptive comments to complex Python scripts and SQL queries. This drastically improves maintainability and knowledge sharing, saving you the tedious manual effort.

Narrative Summary Generation

After you've built a killer dashboard, feed the key charts and data points to an AI model and ask it to draft a high-level executive summary. This gives you a strong first draft for stakeholder communications and presentations, letting you focus on refining the message and adding your unique insights.

Common questions

How do you become an Analytics Manager?

Common routes in include Junior Data Analyst / Associate Technical Analyst (2-3 years), Product Analyst (entry-level) (2-4 years) and Data Scientist (entry-level, with strong analytics focus) (2-3 years). Times vary with prior experience.

Where can an Analytics Manager progress to?

This role can lead on to Senior Analytics Manager (2-3 years) and Staff/Lead Analyst (3-5 years), depending on the skills you build.

What new skills matter most for an Analytics Manager?

Increasingly, Prompt Engineering & LLM Integration. 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

Prompt Engineering & LLM Integration

Skills this role draws on

Technical

How you actually get there, here

Junior Data Analyst / Associate Technical Analyst

Product Analyst (entry-level)

Data Scientist (entry-level, with strong analytics focus)

The credential is what you can do today. These are what keep you valuable.

Roles this leads to

One to one, on the work you already do

A year from now, you're the trusted voice in the room, effortlessly bridging the gap between raw data and strategic action.

Everything else the record holds

The career path, and where it branches

Senior Analytics Manager (L3)

Staff/Lead Analyst (L4 - Individual Contributor path)

Where AI is starting to help

SQL Query Generation

Anomaly Detection & Root Cause Analysis

Documentation & Code Commenting

Narrative Summary Generation

Common questions

Want a read on where you stand?