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

Analytics Support Manager

As an Analytics Support Manager, you ensure data flows seamlessly and users trust the insights they gain.

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

Free to start, no card.

Keep this job description

Where should we send it?

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

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

Also advertised as Manager, Data Support · Head of Analytics Operations · Principal Analytics Support Analyst · Lead, BI Support

Built on 43,079 real UK job descriptions we analysed · grounded in what real qualifications cover

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 4 of 11 standards

Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 4 of 11 standards

Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 2 of 11 standards

NOCN Level 5 Certificate in Productivity Improvement Practice NOCN · Level 5 · covers 1 of 11 standards

NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 8 of 11 standards

OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 6 of 11 standards

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

The units you would start with

Introduction to Data Science and Big Data NCC Education Level 5 Extended Diploma in Computing with Data Science

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

Data Analysis in support of Productivity Improvement Projects NOCN Level 5 Certificate in Productivity Improvement Practice

Data Analytics Primer NOCN Level 4 certificate for Data Analysts

Data Analysis and Visualisation OTHM Level 7 Diploma in Data Science

The future skills this job leans on

Data Fluency turning numbers into a decision

AI Governance Fluency using AI responsibly

Your Plan Illustration

Built for Analytics Support Manager

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

Introduction to Data Science and Big Data NCC Education Limited · covers 4 of 11 standards Level 5
Data Analytics Pearson Education Ltd · covers 4 of 11 standards Level 5
Data Analysis in support of Productivity Improvement Projects NOCN · covers 1 of 11 standards Level 5
Data Analytics Primer NOCN · covers 8 of 11 standards Level 4
Data Analysis and Visualisation OTHM Qualifications · covers 6 of 11 standards Level 7

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 Support 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 starting to handle routine data monitoring and basic troubleshooting, reducing the volume of simple support tickets.

→

Rising: worth more because of AI

Your ability to interpret complex data issues and communicate their impact in a business context becomes even more valuable.

The new skill this role is being asked for

Data Mesh Principles & Decentralised Governance

Why each one is rising

The future skills this job leans on

Data Fluencyturning numbers into a decision
AI Governance Fluencyusing AI responsibly

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 Support 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 feel the weight of ensuring that data is not just available, but reliable and meaningful for everyone who depends on it. With AI's growing role, you wonder how much of your expertise will still be needed.

An Analytics Support Manager leads a team that keeps business data reliable and easy to use. You make sure dashboards work and users can trust the numbers they see. The job involves fixing problems fast and helping people get answers from data.

4A day in the life

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

08:45

You kick off the day with a team huddle, setting the tone and priorities for tackling the day's most pressing data issues.

11:00

A complex data outage arises, and you step in to guide your team through the technical maze, ensuring clear communication with senior stakeholders.

14:30

You meet with Data Engineering to discuss systemic data quality issues and advocate for platform improvements.

16:00

You review the analytics support strategy, refining SLAs and operational metrics to better serve the business needs.

5What you'd actually use

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

SQL (PostgreSQL, T-SQL) Strategic

You won't be writing complex queries daily, but you'll need to understand query execution plans, advise on indexing strategies, and contribute to SQL style guides for the entire analytics organisation. You'll review your team's complex queries and guide them on optimisation.

BI Platforms (Tableau, Power BI, Looker) Architect

Managing user permissions and row-level security models, evaluating new BI tools for the organisation, governing content promotion from dev to prod environments, and setting best practices for dashboard development and maintenance.

Ticketing Systems (Jira Service Management, Zendesk) Strategic

Designing the entire support workflow, reporting on team performance metrics (CSAT, TTR) to leadership, and integrating Jira with other systems like Slack and Confluence to streamline operations.

Knowledge Base (Confluence, Notion) Strategic

Owning the information architecture of the entire knowledge base, implementing strategies to increase documentation usage, and driving initiatives to reduce ticket volume through self-service content.

Data Warehouse (Snowflake, BigQuery, Redshift) Expert

Collaborating with Data Engineering on warehouse architecture, managing data access controls and query cost monitoring, and advising on data governance policies. You'll understand the impact of data modelling decisions on the entire analytics ecosystem.

Spreadsheets (Excel, Google Sheets) Strategic

Understanding the risks of 'spreadsheet marts' (critical data living only in spreadsheets) and developing strategies to migrate critical analysis into governed BI tools. You'll still use them for ad-hoc analysis or quick data validation, but your focus is on scalable solutions.

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 Hiring & Performance	No authority. Assists with interview scheduling.	Provides feedback on candidates. No hiring authority.	Interviews and provides strong recommendations. May mentor new hires.
Budget Allocation (Team Tools & Training)	No authority. Can suggest training.	Can propose small training courses for self.	Recommends tools or training for specific projects (up to £1K).
Process & Workflow Changes	Follows established processes.	Can propose minor tweaks to existing runbooks.	Designs and implements new support workflows for specific issues. Updates knowledge base articles.
Escalation & Stakeholder Communication	Escalates all non-routine issues to supervisor.	Handles routine stakeholder communication. Escalates complex issues.	Manages communication for complex incidents. Consults with leads on high-priority issues.
Strategic Direction of Support Function	No input.	Provides feedback on proposed changes.	Contributes ideas for process improvements and self-service 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.

BI Platform Adoption Rate

The percentage of active business users engaging with our core BI platforms (e.g., Tableau, Power BI) weekly.

Target · Increase weekly active users by 20% year-on-year.

If we had 500 active users last year, we'd aim for 600 this year. You'd track this via platform usage logs and report on trends.

Overall Ticket Volume Reduction

The total number of inbound support tickets related to analytics and data issues.

Target · Decrease overall inbound ticket volume by 15% quarter-on-quarter through self-service enablement and proactive fixes.

Reducing from 1000 tickets in Q1 to 850 in Q2, ideally by improving documentation or fixing systemic issues, not just by ignoring requests!

Team CSAT Score (Average)

The average satisfaction rating from users after their support tickets have been resolved by your team.

Target · Maintain an average user satisfaction rating of >4.5 out of 5.

If your team resolves 200 tickets in a month, and the feedback averages 4.6, you're doing well. It's about quality of interaction, not just speed.

SLA Adherence (Team Average)

The percentage of tickets where your team provides a first response within the agreed service level agreement (e.g., 1 hour for high priority).

Target · Achieve >95% first response within 1 hour for high-priority tickets, and >90% resolution within 24 hours for medium priority.

If 100 high-priority tickets come in, you'd expect 95+ to get a response within an hour. This shows your team's responsiveness.

Knowledge Base Utilisation

The percentage of user queries resolved by users themselves through the knowledge base, without needing to raise a ticket.

Target · Increase KB article views leading to ticket deflection by 10% quarter-on-quarter.

If 100 users search for 'sales dashboard definition' and 10 of them don't raise a ticket afterwards, that's 10% deflection. You'll track this via search logs and follow-up surveys.

Team Morale & Retention

The overall health, engagement, and stability of your support team.

Low team attrition (below 10% annually), positive feedback in 1-on-1s and engagement surveys, active participation in team meetings, and a clear sense of psychological safety within the team. Your team members should feel supported and challenged, not just burnt out by constant firefighting.

Proactive Problem Solving

Your ability to identify and address systemic issues before they escalate into widespread problems or a flood of tickets.

Regularly presenting insights from ticket trends to Data Engineering or Product teams, leading initiatives to fix recurring bugs, and implementing new monitoring or alerting systems that catch issues before users do. You'll be recognised for preventing problems, not just fixing them.

Stakeholder Trust & Partnership

How much business stakeholders rely on your team and see you as a strategic partner, not just a reactive service desk.

Business leaders actively seeking your team's input on new dashboard designs or data initiatives, positive feedback in quarterly business reviews, and a decrease in 'urgent' ad-hoc requests as users become more self-sufficient and trust your team's processes. They'll come to you for advice, not just complaints.

Process Improvement & Scalability

Your success in optimising support processes, automating tasks, and implementing solutions that scale with the business.

Documented improvements to incident management workflows, successful implementation of new automation tools (like AI for triage), a clear roadmap for self-service enablement, and a reduction in manual, repetitive tasks for your team. You're building a machine, not just running one.

8Would you like it

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

What people enjoy

Building and Empowering a Team

You'll spend a good chunk of your day coaching team members, helping them unstick from tough problems, and celebrating their successes. You'll also be actively involved in hiring and developing career paths for your direct reports.

Seeing a junior analyst you mentored successfully lead a complex incident response, or getting positive feedback from a team member about their growth in the role.

Driving Systemic Improvement

You'll be analysing ticket trends, identifying recurring problems, and then working with Data Engineering or Product to implement long-term fixes. This isn't just about closing tickets; it's about making them disappear permanently.

Successfully reducing the number of tickets related to a specific dashboard by 70% after implementing a new data validation check upstream.

Increasing Organisational Data Literacy and Trust

You'll be championing self-service, ensuring our knowledge base is top-notch, and probably running workshops or training sessions for business users. Your goal is to make data accessible and understandable for everyone.

Receiving feedback from a business unit head that their team now feels much more confident using our dashboards and relying on the data for their decisions.

What frustrates people

Constant firefighting that prevents you from focusing on strategic improvements.
Lack of resources or budget to implement the tools and headcount your team truly needs.
Dealing with 'silent upstream changes' where Data Engineering or Product make changes that break your dashboards without warning.
Being caught in the middle of inter-departmental disagreements about data definitions or ownership.
The perception that your team is 'just IT support' rather than a critical enabler of business insights.
Managing team burnout when there's a sustained period of high-priority incidents.

What this role does not give you

A quiet, predictable work environment with minimal interruptions.
The chance to be a deep, individual technical contributor on a daily basis (your focus shifts to leading and enabling others).
Complete control over all data pipelines and source systems (you'll influence, but not directly own, everything).
A role where all your projects see immediate, tangible production impact (some strategic work is long-term and foundational).

9Who you work with

This role is critical for maintaining trust in our data. You'll directly influence the speed and quality of business decisions by ensuring our analytics tools are always available and accurate. You'll also drive our shift towards a more self-service model, meaning fewer ad-hoc requests and more empowered users. Frankly, without a solid analytics support function, our data strategy falls apart.

Inside the business

Director, Analytics Enablement (your boss)
Data Engineering Leads (they build the pipelines)
Business Unit Heads (Sales, Marketing, Finance – your main 'customers')
Product Managers (they own the BI tools and features)
Security & Compliance Teams (for data access and governance)

Outside the business

Key BI platform vendors (Tableau, Power BI, Looker)
Industry peers and professional networks (for best practices)

10What you need before you start

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

Proven experience (at least 3-5 years) in a Senior or Lead Analytics Support Analyst role, demonstrating a strong track record of complex problem-solving and process improvement.
Demonstrable experience managing or mentoring a small team, or leading significant projects with indirect reports.
A deep understanding of data warehousing concepts, BI tool administration, and SQL at an advanced level.
Experience in designing and implementing support processes, SLAs, and knowledge base content.
A track record of successfully engaging with senior business stakeholders and managing expectations during critical incidents.

11What to practise next

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

Advanced AI/ML for Predictive Support

Beyond basic AI for triage, we'll see AI move into predicting potential data issues before they occur. This means using machine learning to analyse historical data patterns, system logs, and user behaviour to flag anomalies or predict outages, shifting support from reactive to truly proactive.

Anomaly Detection Algorithms: Identifying unusual · Predictive Maintenance for Data Pipelines: Using M · Natural Language Generation (NLG) for Incident Rep · Reinforcement Learning for Optimising Support Work · Explainable AI (XAI): Understanding why the AI mad

This quarter: Research current AI-powered ITSM tools and their predictive capabilities.
Next 6 months: Pilot an anomaly detection tool on a critical data source or dashboard.
Next year: Work with Data Science to explore building internal predictive models for common data issues.
Ongoing: Encourage your team to experiment with AI tools for report generation and problem diagnosis.

Quick win: Identify the top 3 recurring data issues that take the most time to diagnose. Brainstorm how AI could help predict or diagnose these faster.

Data Observability Platforms

As data ecosystems grow, simply monitoring dashboards isn't enough. Data observability tools provide end-to-end visibility into data health, lineage, and quality across the entire data stack, allowing for much faster detection and resolution of issues.

Data Freshness & Latency Monitoring: Ensuring data · Schema Evolution Tracking: Monitoring changes to d · Data Volume & Distribution Anomalies: Spotting une · Automated Data Lineage Mapping: Visualising data f · Proactive Alerting & Root Cause Analysis: Automate

This quarter: Research leading data observability platforms (e.g., Monte Carlo, Datafold).
Next 6 months: Engage with Data Engineering to understand their roadmap for data quality and monitoring tools.
Next year: Lead a proof-of-concept for a data observability platform, focusing on how it reduces your team's incident response time.
Ongoing: Advocate for dedicated budget and resources for robust data observability.

Quick win: Map out the current 'blind spots' in our data monitoring. Where do issues typically go unnoticed until a user complains?

12Staying current once you are in

What people here do to keep up

Actively participate in industry meetups, conferences, and online communities focused on data operations, analytics engineering, or BI governance.
Take advanced courses in leadership, change management, or strategic planning to hone your management skills.
Regularly engage with thought leaders and read publications on emerging trends in data management, AI for operations, and self-service analytics.
Seek out opportunities to mentor junior professionals, even outside of your direct team, to further develop your coaching abilities.

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.

Data Mesh Principles & Decentralised Governance

Organisations are moving away from centralised data lakes to more decentralised 'data mesh' architectures, where data ownership shifts closer to the domain teams. This fundamentally changes how data is produced, consumed, and supported.

Data as a Product: Treating data sets as products
Domain-Oriented Ownership: Data ownership by the b
Self-Serve Data Platform: Providing tools and infr
Federated Governance: Decentralised decision-makin
Data Contracts: Formal agreements on data schema a

14What you’ll use

What you’ll use

Skills this role draws on

Technical

Strategic Stakeholder Triage & Management
Data Governance & Quality Strategy
BI Tool Architecture & Governance
Proactive Data Monitoring & Alerting
User Acceptance Testing (UAT) Oversight

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 Analytics Support Analyst (L3) or Lead Analytics Support Analyst (L4)
3-5 years as a Senior/Lead Analyst
Skills to master
- Deep technical problem-solving, incident management, process improvement, informal team leadership (mentoring), and effective stakeholder communication.
You're ready to move on when
- You're the go-to person for the toughest technical issues on your current team.
- You've successfully led cross-functional projects to improve data quality or support processes.
- You've formally mentored junior analysts and seen them grow under your guidance.
- You're regularly presenting technical solutions and recommendations to non-technical audiences.
2
Technical Account Manager or Technical Support Lead (from a vendor)
5-7 years in a similar client-facing technical leadership role.
Skills to master
- Client relationship management, deep product knowledge (especially BI tools), technical troubleshooting, and managing complex escalations.
You're ready to move on when
- You've managed a portfolio of enterprise clients and handled their most critical technical issues.
- You have a strong understanding of how BI tools are used in large organisations and the common challenges.
- You're adept at translating technical problems into business impact and vice-versa.
- You've led a small team or a significant technical project for a vendor.
3
Analytics Engineer or Data Operations Lead (with a strong support focus)
4-6 years in an engineering/operations role, with significant user interaction.
Skills to master
- Data pipeline development, data quality monitoring, infrastructure management, and a strong empathy for end-user challenges.
You're ready to move on when
- You've built and maintained robust data pipelines, but you also spend time understanding how your work impacts end-users.
- You're passionate about data quality and have implemented monitoring solutions.
- You've acted as a bridge between data engineers and business users, translating requirements and troubleshooting issues.
- You've shown an interest in improving the 'last mile' of data delivery to users.

16How people get here · where they go next

Came from

Senior Analytics Support Analyst (L3) or Lead Analytics Support Analyst (L4)

3-5 years

You mastered deep technical problem-solving and became the go-to person for the toughest issues.

You are here

Analytics Support Manager

Principal/Manager (12-16 years)

Goes to

Director, Analytics Enablement (L6)

3-5 years

This role expands your scope to owning the entire BI user experience, including data literacy and governance.

The long view: This role can lead to senior data leadership positions, such as Chief Data Officer or Head of Data Governance and Quality.

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 Support 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 Introduction to Data Science and Big Data, worked against Analytics Support Manager.

Introduction to Data Science and Big Data Level 5

Applied to your work in Analytics Support Manager

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

The Navigator Last time, we discussed how decentralised data ownership could change your support model. How has that conversation evolved with your team?

You We've started identifying which data domains could benefit from this approach.

The Navigator Great, let’s map out the stakeholders for one key domain and plan a pilot to test this support model.

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.

A year from now

A year from now, you confidently lead a team that not only resolves data issues but also empowers users with self-service analytics capabilities.

See Your Progress Grow Illustration

Analytics Support Manager

Strategic Stakeholder Triage & Management
Data Governance & Quality Strategy
BI Tool Architecture & Governance
Proactive Data Monitoring & Alerting
User Acceptance Testing (UAT) Oversight

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.

17The 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 Support Manager is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

Director, Analytics Enablement (L6)
3-5 years as Analytics Support Manager

You'll move from managing the support function to owning the entire BI user experience, including data literacy, training, governance, and the overall strategy for how data is consumed across the organisation. This role has a broader scope and larger P&L responsibility.
- Enterprise Data Governance Frameworks Designing and implementing company-wide data policies.
- Advanced Data Literacy Programme Design Creating scalable training for all levels of the business.
- BI Tool Ecosystem Strategy Evaluating and selecting tools that fit the long-term vision.
- Data Product Management Overseeing the lifecycle of key data products.

Working with AI on the job

Working with AI

Where AI is starting to help

As an Analytics Support Manager, you're always looking for ways to make your team more efficient and proactive. AI isn't just a buzzword; it's a practical tool that can transform how your team operates, freeing them up from repetitive tasks to focus on deeper problem-solving and strategic initiatives.

Imagine a world where your team spends less time on manual ticket triage and more time solving the root causes of data issues. AI tools, when implemented smartly, can dramatically reduce the operational burden, allowing your analysts to be more impactful and less prone to burnout. This means better service for our users and a more engaged team for you.

Automated Ticket Triage & Routing

An AI model can instantly read incoming support tickets, identify keywords like 'Tableau permissions' or 'slow query', then automatically assign priority, categorise the issue, and route it to the right specialist. It can even suggest relevant knowledge base articles to the user immediately, often deflecting tickets before they even reach your team. This means your team starts working on the right problem, faster.

Root Cause Analysis Accelerator

When a dashboard goes down, an AI agent can quickly scan system logs, query histories, and recent code changes across our data platforms. It then provides your team with a ranked list of probable causes, like 'ETL job failed at 3 AM' or 'recent deployment introduced a breaking change'. This drastically cuts down investigation time, getting us to a solution much quicker.

Natural Language Query Explanation

Your team often has to explain complex SQL queries to non-technical stakeholders. With AI, they can paste a 200-line query and ask the tool to 'explain this to a sales manager'. It'll generate a simple, bullet-pointed summary, saving valuable time and ensuring everyone understands what the data represents. This helps bridge the gap between technical and business teams.

First-Draft Documentation Generator

Creating comprehensive user documentation for new dashboards or data sets is vital but time-consuming. An AI tool can analyse a new dashboard's charts, filters, and underlying data fields to generate a structured first draft, explaining each component and metric. Your team then refines it, saving hours and ensuring our knowledge base is always up-to-date and useful.

Common questions

How do you become an Analytics Support Manager?

Common routes in include Senior Analytics Support Analyst or Lead Analytics Support Analyst (3-5 years as a Senior/Lead Analyst), Technical Account Manager or Technical Support Lead (from a vendor) (5-7 years in a similar client-facing technical leadership role.) and Analytics Engineer or Data Operations Lead (with a strong support focus) (4-6 years in an engineering/operations role, with significant user interaction.). Times vary with prior experience.

Where can an Analytics Support Manager progress to?

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

What new skills matter most for an Analytics Support Manager?

Increasingly, Data Mesh Principles & Decentralised Governance. 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 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.

Analytics Support Manager

Where should we send it?

Two short quizzes about you

Will AI change your work as an Analytics Support Manager?

What's rising in its place

Data Mesh Principles & Decentralised Governance

Skills this role draws on

Technical

How you actually get there, here

Senior Analytics Support Analyst (L3) or Lead Analytics Support Analyst (L4)

Technical Account Manager or Technical Support Lead (from a vendor)

Analytics Engineer or Data Operations Lead (with a strong support 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 confidently lead a team that not only resolves data issues but also empowers users with self-service analytics capabilities.

Everything else the record holds

The career path, and where it branches

Director, Analytics Enablement (L6)

Where AI is starting to help

Automated Ticket Triage & Routing

Root Cause Analysis Accelerator

Natural Language Query Explanation

First-Draft Documentation Generator

Common questions

Want a read on where you stand?