Marketing Data Analytics Engineer

Design, build from the ground up, and govern the end‑to‑end marketing data foundation, including tagging, tracking, data pipelines, and modelling, to create a trusted single source of truth across brand direct, paid media, social, and CRM.

This role is accountable for establishing greenfield data flows, standards, and operating models, enabling faster, better decision‑making through accurate data, integrated views (GA4, Salesforce, Databricks, COGNOS), and scalable insight products (dashboards, attribution, MMM). Act as the primary point of contact for all digital tracking and marketing data issues, ensuring reliability, compliance, and speed across the ecosystem.

Own tagging and tracking standards for web/app (GTM, GA4, CM360/Floodlight, Meta pixel/event manager, consent mode) Define and maintain the marketing KPI dictionary and data model; steward the single source of truth. Define data pipelines between martech platforms and enterprise solutions (Salesforce, COGNOS, Databricks). Set QA/alerting SLAs, prioritise analytics backlog. Advise on experimentation, attribution and MMM, recommend budget reallocations based on evidence.

Key Responsibilities:

• Design and build the marketing data foundation from scratch, including tracking architecture, event schemas, identity strategy, and data flows across martech and enterprise platforms.
• Tagging and implementation: Deploy and audit events, conversions, and consent, server-side GTM evaluation, manage parameter standards and de-duplication rules
• Platform integrations: Build robust connectors/APIs for GA4, GMP (CM360/DV360/SA360), Meta and other platforms. Unify with Databricks, COGNOS and Salesforce
• Data engineering: Model clean tables/views, implement data quality checks and documentation
• Dashboards and reporting: Deliver looker studio and Tableau dashboards, automate recurring reporting, provide training to channel owners
• Attribution and MMM: Deploy open source MMM (Meta Robyn, Google Meridian), design holdouts, support hybrid attribution and incrementality studies
• Governance and compliance: Ensure GDPR/Consent compliance, maintain audit trails, partner with legal on risk mitigation
• Troubleshooting and enablement: Act as a single point of contact for data/tracking issues, triage quickly, run enablement sessions and documentation

Key KPIs

• Tag coverage rate and accuracy; reduced data discrepancy between platforms and data sources
• Pipeline uptime and latency SLAs; time to lag and time to insight reductions