Responsibilities

• Marketing Measurement & Attribution 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 MarTech Data & Infrastructure Act as the data lead for Moneybox's marketing technology stack, assessing the current state of play and designing the roadmap to make improvements to the data underpinning Partner closely with Analytics Engineering and Data Engineering teams to ensure martech data flows (AppsFlyer, Google Ads API, Meta Ads API, GCP, Mixpanel) are well-modelled, documented, and trusted Contribute to the design and governance of marketing data models within the broader data platform (Databricks), including gold/silver/bronze layer definitions relevant to marketing use cases Identify and close gaps in marketing data coverage; define requirements for new integrations and own their delivery where appropriate Data Products & Reporting 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 Stakeholder Partnership 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

Requirements

• 5+ years in a marketing analytics, data science, or analytics engineering role with a strong focus on marketing measurement
• Hands-on experience with multi-touch attribution methodologies and media mix modelling
• Strong SQL skills; experience using it for data manipulation, data analysis, and modelling
• Direct experience with mobile attribution platforms, particularly AppsFlyer
• 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)
• Demonstrable experience building data products and self-serve reporting assets used by non-technical stakeholders
• Track record of designing and analysing incrementality experiments or A/B tests in a marketing context

Preferred

• Experience with dbt (Cloud or Core) for transformation layer development
• Familiarity with Databricks or similar modern data lakehouse platforms
• Experience with Power BI (or similar data visualisation tool)
• 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

Benefits

• Hybrid working: 2 days from our London office, 3 from home