United Kingdom · Events and experiential marketing · Senior (5-8 years)

Senior Event Data Analytics Manager

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

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As a Senior Event Data Analytics Manager, a good place to start on Prompt Engineering & LLM Integration for Analysis is Using Data to Direct a Department's Priorities, a free Future Fluency module at Level 5. You'd practise using the data your department already produces to decide where its effort should go.

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Experience bandSenior (5-8 years)
Direct reportsNo direct reports
Reports toManager, Event Analytics & Insights
UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Lead Event Analyst · Data & Insights Specialist (Events) · Experiential Marketing Data Lead

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

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

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

NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 1 of 5 standards

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

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

OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 3 of 5 standards

NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 3 of 5 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

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

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

Data Analysis and Visualisation OTHM Level 7 Diploma in Data Science

Data Analytics Primer NOCN Level 4 certificate 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

Your Plan Illustration

Built for Senior Event Data Analytics Manager

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

Data Analytics Pearson Education Ltd · covers 3 of 5 standards Level 5
Introduction to Data Science and Big Data NCC Education Limited · covers 1 of 5 standards Level 5
Data Analysis in support of Productivity Improvement Projects NOCN · covers 1 of 5 standards Level 5
Data Analysis and Visualisation OTHM Qualifications · covers 3 of 5 standards Level 7
Data Analytics Primer NOCN · covers 3 of 5 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 a Senior Event Data Analytics Manager?

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

The new skills this role is being asked for

Prompt Engineering & LLM Integration for Analysis
Real-time Event Data Streaming & Analytics

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 Senior Event Data Analytics Manager plan as you set up, starting with the free Data Fluency module. No card.

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

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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

This job is about making sense of event data and turning it into clear, actionable insight. You dig into the numbers, find the reasons behind them, and help decide where event budgets should go. You handle complex analysis and help guide others doing similar work.

4What you'd actually use

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

Cvent, Bizzabo, Splash, Hopin (Event Management Platforms) Expert

Building custom reports, integrating platform data with other systems via APIs, troubleshooting complex data flow issues, and extracting granular attendee behaviour data.

Salesforce (Sales/Service Cloud), HubSpot, Marketo (CRM & Marketing Automation) Advanced

Building complex reports/dashboards, writing SOQL queries, managing data integration logic between event platforms and CRM, and designing attribution campaign structures.

Tableau, Power BI, Looker Studio (BI & Data Visualisation) Expert

Connecting to multiple complex data sources (SQL, APIs), using advanced calculations (like LODs in Tableau), and building interactive, drill-down dashboards that tell a clear story for executive audiences.

SQL (PostgreSQL, T-SQL), Excel (Power Query, XLOOKUP, Pivot Tables) Advanced

Writing complex CTEs, window functions, and stored procedures to extract and transform data. Mastering Power Query for advanced ETL and building complex financial models in Excel for ROI calculations.

Python (pandas, Matplotlib, scikit-learn), R (Advanced Analytics & Scripting) Intermediate

Using pandas for advanced data wrangling, building predictive models (e.g., churn, attendance likelihood), and automating reporting tasks that can't be done in BI tools.

Qualtrics, SurveyMonkey, Typeform (Survey & Feedback Tools) Advanced

Implementing complex survey logic (branching, piping), integrating survey data with CRM, and performing sentiment analysis on open-text feedback to extract qualitative insights.

Snowflake, Anaplan, Collibra (Executive & Planning Platforms) Awareness

Understanding how your event data feeds into these enterprise systems and knowing how to access relevant data from them for your analyses. You won't be an architect, but you'll know how to use the data.

5What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choice	Coming in	Where you are now	The step above
Technical Methodology (e.g., statistical model choice)	Propose options, seek approval from senior analyst/manager.	Choose methodology for routine problems, consult for novel ones.	Full authority within project scope; consult manager on enterprise-wide standards.
Data Source Integration	Identify potential sources, ask senior analyst to integrate.	Integrate standard data sources (e.g., CSV imports), escalate API challenges.	Design and implement integration pipelines for complex sources; recommend new tools/APIs.
Reporting & Dashboard Design	Build dashboards from existing templates with guidance.	Design new dashboards for specific project needs, get manager review.	Design and own key strategic dashboards; set best practices for team reporting.
Budget for New Tools/Training	Request specific training/tool access from manager.	Propose small-scale tool trials (<£500), seek manager approval.	Recommend budget spend up to £5K for new tools or data sources; present business case for larger investments to manager.
Mentorship & Guidance to Junior Staff	N/A	Offer informal help to new joiners on basic tasks.	Provide formal mentorship, conduct code reviews, guide project work for 1-2 junior analysts.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

Multi-Touch Attribution Model Accuracy

How accurately your attribution models assign credit to events for generated pipeline and closed-won revenue, compared to actual sales data.

Target · Achieve >85% accuracy in event-influenced pipeline attribution.

Your Q3 attribution model predicted £2M in event-influenced pipeline; actual sales data confirmed £1.9M was influenced, meaning 95% accuracy.

Event-Influenced MQL Conversion Rate Improvement

The percentage increase in conversion rates from event-generated leads to Marketing Qualified Leads (MQLs) as a direct result of your audience segmentation and targeting recommendations.

Target · Improve event-sourced MQL conversion by 15% year-on-year.

After implementing your new segmentation model, leads from Tier 1 events saw a jump from 8% to 9.2% MQL conversion in 6 months.

Reduction in Manual Reporting Effort

The amount of time saved across the team by automating key data pipelines and reporting processes that you've designed and implemented.

Target · Reduce total manual reporting hours by 40% within 12 months for your managed workstreams.

Automating the weekly event performance dashboard cut 10 hours of manual work for the junior analyst each week.

Predictive Model Accuracy (Attendance/Engagement)

The accuracy of your models in forecasting event attendance, no-show rates, or post-event engagement likelihood.

Target · Maintain a predictive accuracy of ±10% for key event attendance forecasts.

Your model predicted 500 attendees for the Q2 summit; actual attendance was 480, well within the target range.

Stakeholder Trust & Adoption of Insights

The degree to which event leadership and other teams proactively seek your input and act on your data-driven recommendations.

You're regularly invited to strategic planning meetings, your opinions are sought on major event decisions, and your recommended changes are implemented. You'll see your dashboards being used in presentations by others without you prompting them.

Mentorship & Team Development

Your effectiveness in guiding and developing junior analysts, helping them grow their skills and confidence.

Junior team members regularly approach you for advice, they show measurable improvement in their analytical skills and autonomy, and they successfully take on more complex tasks under your guidance. Feedback from your manager will highlight your coaching abilities.

Proactive Problem Identification

Your ability to spot potential data issues, reporting discrepancies, or underperforming event elements before they become major problems.

You're raising flags about data quality or event performance trends before anyone else. You're bringing solutions to the table, not just problems. For instance, you might spot a drop in post-event survey completion rates and propose a fix before the next event.

Clarity of Data Storytelling

How well you translate complex analytical findings into clear, compelling narratives that non-technical audiences can understand and act upon.

Event leaders consistently praise the clarity of your presentations and reports. Your recommendations are easily understood and adopted by teams like Sales or Marketing without needing extensive follow-up explanations. People often say, 'Ah, now I get it!' after you've explained something.

7Would you like it

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

What people enjoy

Making a Tangible Impact

You'll feel most satisfied when your analysis directly leads to a change in event strategy, a better-performing campaign, or a more efficient use of budget. Seeing your recommendations implemented and proving their worth is a huge win for you.

You recommended shifting budget from a poorly performing webinar series to a high-engagement executive dinner programme, and the next quarter's pipeline numbers proved you right.

Solving Complex Puzzles

You thrive on the challenge of untangling messy datasets, stitching together disparate information, and figuring out the 'why' behind complex event behaviours. The harder the data problem, the more engaged you'll be.

You spent weeks figuring out how to accurately attribute revenue across five different event touchpoints, finally cracking the 'stitching problem' for our biggest client event.

Mentoring & Guiding Others

You enjoy sharing your knowledge and helping junior team members grow. You get a real kick out of seeing someone you've mentored 'get it' or successfully deliver a complex piece of analysis on their own.

A junior analyst you've been coaching successfully presented their first in-depth post-event report to a senior stakeholder, and you felt immense pride in their achievement.

What frustrates people

The Black Hole of Sales Follow-Up: Building a perfect attribution model only to have it fail because sales reps don't consistently log their event-related activities in Salesforce. It's infuriating.
Garbage In, Garbage Out: Receiving a third-party conference attendee list in a nightmarish CSV format that takes two days to clean before any analysis can even begin. Yes, it's as tedious as it sounds.
The 'Quick Number' Ambush: That 4:30 PM email from a VP asking for a 'quick number' for their morning presentation, which actually requires stitching together three datasets and validating the logic. Your evening plans? Gone.
Disconnected Tech Stack: Wrestling with APIs and manual CSV exports to connect data from Cvent, Salesforce, and Marketo, which were all purchased by different teams with no thought to integration. It's a constant battle.
Political Pressure for 'Good News': Being implicitly or explicitly pressured to frame data to make a high-cost, high-visibility event look successful, even when the numbers clearly show it was a flop. Integrity is key here.
Fighting the 'Butts in Seats' Mentality: Constantly having to educate stakeholders that the number of registrants is a vanity metric and that quality of attendance and engagement is what truly matters. It's an uphill battle sometimes.
The 'Badge Scanner Was Down' Excuse: The classic data gap that renders booth traffic analysis for a key sponsor completely impossible, leaving you to piece together a story from anecdotal evidence. You'll want to pull your hair out.

What this role does not give you

A perfectly clean, pre-organised dataset handed to you every morning.
Guaranteed implementation of every single insight you uncover.
A quiet, uninterrupted environment where you can just focus on deep analytical work without urgent requests.
A role where you only deal with numbers and never have to 'sell' your ideas to people.

8Who you work with

This role directly influences the effectiveness and ROI of our entire Events_Experiential_Marketing portfolio. Your insights will guide budget allocation, event design, and post-event follow-up strategies, ultimately driving more efficient pipeline generation and revenue attribution for the business. Get it right, and we save millions; get it wrong, and we waste millions.

Inside the business

Head of Events
Event Marketing Managers
Sales Leadership
Product Marketing
Finance Business Partners

Outside the business

Event Technology Vendors (e.g., Cvent, Bizzabo)
Survey Tool Providers (e.g., Qualtrics)
Data Integration Partners

9What you need before you start

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

Proven track record (5+ years) of delivering complex data analysis in a marketing or events context, demonstrating clear business impact.
Demonstrable expertise in SQL for complex data querying and manipulation, including CTEs and window functions.
Advanced proficiency in at least one major BI tool (Tableau or Power BI), including dashboard design, complex calculations, and connecting to multiple data sources.
Solid understanding of statistical concepts (e.g., hypothesis testing, regression) and their application in business decision-making.
Experience presenting analytical findings and recommendations to senior, non-technical audiences, with evidence of influencing decisions.
Ability to work independently, manage multiple priorities, and proactively identify and solve problems with minimal supervision.

10What to practise next

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

Advanced Python/R for Machine Learning

As our data volume grows, manual analysis won't cut it. You'll need to move beyond basic scripting to build more sophisticated predictive models (e.g., event churn prediction, lead scoring for event attendees) and automate complex data transformations. This is where Python/R really shines.

Scikit-learn for Predictive Modelling · Feature Engineering · Model Evaluation & Interpretation

This quarter: Complete an online course on machine learning with Python (e.g., Coursera, DataCamp).
Next quarter: Build a simple event no-show prediction model using historical registration data and evaluate its accuracy.
Month 6: Experiment with different feature engineering techniques to improve your model's performance.

Quick win: Start using Python for more complex data cleaning and transformation tasks that Excel or SQL struggle with. Automate a weekly report generation script.

Data Governance & Metadata Management

As we collect more data from more sources, knowing what data we have, where it came from, and who owns it becomes critical. Good data governance prevents errors, ensures compliance, and makes everyone's life easier. You'll need to understand how to contribute to a structured data environment.

Data Lineage · Data Catalogue & Glossary · Data Quality Rules

This month: Familiarise yourself with our internal data dictionary (if we have one) or start building one for event-specific terms.
Next quarter: Participate in a data governance working group, offering insights from the events perspective.
Month 6: Propose and implement 2-3 new data quality rules for our event registration data.

Quick win: Start consistently documenting your data sources, transformations, and definitions for every analysis you perform. It's a small habit with big long-term benefits.

11Staying current once you are in

What people here do to keep up

Regularly attending industry webinars and conferences focused on marketing analytics, event tech, or data science. Staying current is crucial.
Actively participating in online communities or forums for data professionals (e.g., Stack Overflow, Kaggle) to learn from peers and solve problems.
Taking advanced online courses on specific topics like machine learning, advanced SQL, or data visualisation best practices.
Reading relevant books and articles on data storytelling, statistical analysis, and the psychology of events. It's not just about the tools, it's about the thinking.
Seeking out opportunities to mentor junior colleagues or present your work to internal teams, honing your communication and leadership skills.

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Prompt Engineering & LLM Integration for Analysis

Honestly, competitors are already using tools like ChatGPT and Claude to draft reports in 10 minutes that used to take 2 hours. Analysts who figure out how to effectively use Large Language Models (LLMs) will outproduce their peers significantly. This isn't future-gazing; it's happening now.

Context Windows & Token Limits
Temperature Settings for Different Tasks
RAG (Retrieval Augmented Generation) Architectures
Output Validation & Hallucination Detection
Prompt Chaining for Complex Analysis

Real-time Event Data Streaming & Analytics

Attendees expect personalised experiences, and event organisers need to react instantly. Moving from post-event analysis to real-time insights during an event is becoming a competitive differentiator. Imagine adjusting session capacity or marketing messages during an event based on live engagement data.

Event Stream Processing
Low-Latency Data Pipelines
Real-time Dashboards
Anomaly Detection

13What you’ll use

What you’ll use

Skills this role draws on

Technical

Multi-Touch Attribution (MTA) Modelling
Lead-to-Revenue Funnel Analysis
Audience Segmentation & Persona Analysis
Event ROI & ROO (Return on Objectives) Frameworks
Predictive Modelling for Attendance & Engagement
Data Storytelling & Visualisation

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

1
Event Data Analyst (L2)
2-3 years
Skills to master
- Independently delivering standard post-event analysis, taking ownership of data lifecycle for smaller events, and building robust dashboards from clean data. You'd be showing you can consistently deliver reliable insights.
You're ready to move on when
- Consistently delivers accurate and insightful post-event reports on time.
- Proactively identifies and resolves minor data quality issues.
- Begins to mentor new joiners informally, sharing knowledge and best practices.
- Takes initiative to propose improvements to existing reporting processes.
2
Marketing Analyst (from another industry)
3-5 years (with relevant experience)
Skills to master
- Translating your existing analytical skills to the unique context of events, quickly learning our tech stack (Cvent, Bizzabo), and understanding event-specific metrics like 'dwell time' and 'scan-to-MQL rate'.
You're ready to move on when
- Quickly grasps event-specific data nuances and business objectives.
- Successfully applies existing analytical techniques to event challenges.
- Demonstrates strong initiative in learning new event platforms and data sources.
- Builds credibility with event stakeholders through insightful contributions.
3
Data Scientist (Junior/Mid-level)
2-4 years
Skills to master
- Applying your statistical and machine learning expertise to business-driven event problems, focusing on actionable insights rather than just model complexity, and developing strong data storytelling skills for non-technical audiences.
You're ready to move on when
- Effectively translates complex models into clear, business-relevant insights.
- Demonstrates a strong understanding of event marketing objectives and challenges.
- Proactively seeks opportunities to apply advanced analytics to event data.
- Adapts communication style to suit diverse stakeholder groups.

15Where this role leads

The long view: This work can lead to managing a team of analysts, or into a director role covering the whole experiential marketing portfolio.

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 Senior Event Data 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 Senior Event Data Analytics Manager.

Data Analytics Level 5

Applied to your work in Senior Event Data 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.

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.

See Your Progress Grow Illustration

Senior Event Data Analytics Manager

Multi-Touch Attribution (MTA) Modelling
Lead-to-Revenue Funnel Analysis
Audience Segmentation & Persona Analysis
Event ROI & ROO (Return on Objectives) Frameworks
Predictive Modelling for Attendance & Engagement
Data Storytelling & Visualisation

This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

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

Lead Event Data Strategist (L4)
3-5 years

This is a significant jump, moving from leading analysis to architecting the entire measurement framework and potentially leading a small team. You'll be defining 'how' we measure, not just 'what' we measure.
- Data Architecture Design Architecting the overall data infrastructure for events, including data warehousing and ETL pipelines.
- Advanced Predictive Modelling Leading the development and deployment of enterprise-level predictive models for event optimisation.
- Vendor Management & Negotiation Evaluating and selecting new event tech and data tools, negotiating contracts.
- Data Governance Leadership Defining and implementing data governance policies specific to event data.

Working with AI on the job

Working with AI

Where AI is starting to help

Let's be honest, a lot of data analysis can be repetitive and time-consuming. But what if you could offload some of that grunt work to AI, freeing you up for the really interesting, strategic stuff? We're actively building an AI-first culture, and for a Senior Event Data Analytics Manager, that means supercharging your productivity.

We're not just talking about buzzwords here. We're integrating practical AI tools directly into our daily workflows to automate the tedious, accelerate insights, and give you more time to focus on the 'why' and the 'so what' of our event data. Imagine less time cleaning data and more time building game-changing models.

Attendee List Auto-Cleaner

Use AI tools (like a custom GPT or dedicated software) to automatically standardise job titles, format company names, de-duplicate entries, and flag incomplete records from those notoriously messy third-party registration lists. It's like having a super-fast data assistant for the most tedious part of the job.

AI-Powered Sentiment Analysis

Feed thousands of open-ended survey responses (e.g., 'What did you like most about the event?') into an AI model. It'll instantly categorise feedback, identify key themes (like 'Networking', 'Keynote Speaker', 'Catering'), and even score sentiment without you having to manually read through every single comment. This saves days of work.

Competitor Strategy Synthesizer

Use an AI research assistant to quickly scan and summarise press releases, social media, and industry news about competitor events. Ask it to generate a concise report on their key themes, target audiences, and announced speakers. Get a competitive overview in minutes, not hours.

First-Draft Narrative Generator

Provide an AI model with key data points and charts from your analysis (e.g., 'Attendance: 500, MQLs: 50, Pipeline: £2M, Top Session: 'Future of X''). Ask it to write the first draft of the executive summary for your post-event PowerPoint deck. It's a massive head start on your reporting.

Common questions

How do you become a Senior Event Data Analytics Manager?

Common routes in include Event Data Analyst (2-3 years), Marketing Analyst (from another industry) (3-5 years (with relevant experience)) and Data Scientist (Junior/Mid-level) (2-4 years). Times vary with prior experience.

Where can a Senior Event Data Analytics Manager progress to?

This role can lead on to Lead Event Data Strategist (3-5 years), depending on the skills you build.

What new skills matter most for a Senior Event Data Analytics Manager?

Increasingly, Prompt Engineering & LLM Integration for Analysis and Real-time Event Data Streaming & Analytics. 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.

Senior Event Data Analytics Manager

Where should we send it?

Two short quizzes about you

Will AI change your work as a Senior Event Data Analytics Manager?

What's rising in its place

Prompt Engineering & LLM Integration for Analysis

Real-time Event Data Streaming & Analytics

Skills this role draws on

Technical

How you actually get there, here

Event Data Analyst (L2)

Marketing Analyst (from another industry)

Data Scientist (Junior/Mid-level)

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

Everything else the record holds

The career path, and where it branches

Lead Event Data Strategist (L4)

Where AI is starting to help

Attendee List Auto-Cleaner

AI-Powered Sentiment Analysis

Competitor Strategy Synthesizer

First-Draft Narrative Generator

Common questions

Want a read on where you stand?