2mo ago

Marketing Insight & Analytics Lead - Mat Cover

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OtherAnalytics Lead

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Quick Summary

Overview

About Moneybox At Moneybox, our mission is to give everyone the means to get more out of life. We're guided by our belief that wealth isn't about the money,

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OtherAnalytics Lead

About Moneybox

At Moneybox, our mission is to give everyone the means to get more out of life. We're guided by our belief that wealth isn't about the money, it's about the means to more - more freedom, opportunities, possibilities, and peace of mind. Moneybox is an award-winning wealth management platform, helping over one and a half million people build wealth throughout their lives, whether they’re saving and investing, buying their first home, or planning for retirement.

Job Brief

We're looking for a senior Marketing Insight & Analytics Lead to join the Data & Insight team on a fixed-term basis. This role is dedicated to supporting our Marketing function, acting as the analytical and technical centre of gravity for everything from campaign measurement to martech data infrastructure.

You'll work at the intersection of data engineering, analytics engineering, and marketing strategy. You'll refresh the measurement frameworks that tell us whether our marketing is working, and build the data products that make those answers repeatable and trusted.

Lead the design and build of Moneybox's marketing attribution framework

Own incrementality testing: design experiments, define holdout groups, and translate results into actionable media planning recommendations

Develop standardised effectiveness metrics and reporting that span paid, owned, and earned channels

Own the analytical view of Moneybox’s marketing data, assessing the current state of play and defining the roadmap for improving the data underpinning our measurement

Partner closely with Analytics Engineering and Data Engineering teams to ensure martech data flows (AppsFlyer, Google Analytics, Google Ads API, Meta Ads API, GCP, Mixpanel) are well-modelled, documented, and trusted

Define the requirements for marketing data models within our broader data platform (Databricks) and work with Analytics Engineering on their design and governance

Identify gaps in marketing data coverage and define the requirements for closing them, partnering with Analytics Engineering on delivery

Build and maintain marketing data products – from campaign performance dashboards to customer acquisition cost models – that are used regularly by marketing leads and senior stakeholders

Define and document sources of truth for key marketing KPIs, ensuring consistency across reporting surfaces

Own the marketing reporting layer in Power BI (or equivalent), ensuring outputs are accurate, timely, and interpretable by non-technical audiences

Leverage AI to automate routine reporting, draft performance narratives, and surface key trends or anomalies

Serve as the primary data and analytics partner for the Marketing team, translating commercial questions into analytical briefs and technical requirements

Support media planning cycles with data-driven audience segmentation, channel mix analysis, and budget allocation modelling

Represent the Data & Insight team in cross-functional marketing planning forums, contributing to roadmap prioritisation

A senior individual contributor who is as comfortable building technical solutions as you are communicating them to non-technical stakeholders

Deeply curious about marketing effectiveness: you have opinions about the limits of last-click attribution and the conditions under which MMM is and isn’t trustworthy

A clear communicator who can make complex measurement concepts accessible to marketing and commercial stakeholders without dumbing them down

Excited about fintech and the particular measurement challenges that come with a regulated, app-first, long-consideration financial product

Comfortable with ambiguity and able to operate with autonomy in a fast-paced environment where the brief sometimes evolves mid-sprint

Partnered directly with marketing teams to solve complex growth and measurement challenges – for example evaluating campaign effectiveness, resolving attribution discrepancies, and optimising media spend to maximise long-term customer value

Hands-on experience designing and running media incrementality experiments – geo holdouts, staggered rollouts or platform lift tests – including how you selected control groups, sized the test, and read the result

5+ years in a marketing analytics, data science, or analytics engineering role with a strong focus on marketing measurement

Strong SQL skills; experience using it for data manipulation, data analysis, and modelling

Familiarity with multi-touch attribution methodologies and media mix modelling

Demonstrable experience building data products and self-serve reporting assets used by non-technical stakeholders

If you meet most but not all of the above, we’d still encourage you to apply.

Experience with mobile attribution platforms such as AppsFlyer, Adjust or Branch

Familiarity with the privacy landscape across both app (e.g. iOS SKAdNetwork) and web (e.g. cookie deprecation), and how to navigate the resulting measurement challenges

Experience working with advertising platform APIs (Google Ads, Meta Ads) for data extraction and pipeline automation

Familiarity with GCP data services and event analytics platforms (Mixpanel or equivalent)

Experience with Power BI (or similar data visualisation tool)

Experience with dbt (Cloud or Core) for transformation layer development

Familiarity with Databricks or similar modern data lakehouse platforms

Experience in a regulated financial services or fintech environment

Exposure to customer data platforms (CDPs) or CRM data integration (e.g. Braze)

Experience working within or alongside analytics engineering teams and contributing to shared data models

Understanding of CI/CD practices for data pipelines

Experience with Python for data manipulation, modelling, and pipeline work

Join a fast-growing, award-winning company with genuine ambition to improve people's financial lives

Work in a team that takes data quality and analytical rigour seriously, with a modern stack and strong engineering culture