Marketing Data Analytics Engineer

Job Description

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
• Dashboard adoption and stakeholder satisfaction
• Evidence based budget reallocation % driven by MMM/holdouts; lift from incrementality tests
• Compliance readiness; consent coverage, audit trail completeness

Educational Background

• Degree in Computer Science, Analytics or Data Science

Professional Experience

• 5-8 years in analytics/data engineering or marketing analytics engineering roles
• Expertise in GTM/GA4/GMP/Meta tracking; strong SQL, experience with BigQuery or equivalent
• Hands on APIs
• Proficiency with dashboarding (Looker studio/Tableau) and at least one scripting language (Python or R)
• MMM/Attribution exposure (Robyn, Meridian) and understanding of privacy frameworks (GDPR. Consent mode)

Skills And Competencies

• Structured problem solving, bias to automate and standardise
• Clear communicator who can translate between technical and commercial stakeholders
• Strong ownership and prioritisation; able to manage technical backlog and SLAs
• Documentation discipline; enablement mindset to upskill the wider team

Tools and stack

• BigQuery, Python/R, GA4, CM360/DV360/SA360, Meta
• Looker Studio/Tableau, Serverside GTM, privacy and consent platforms
• Salesforce, COGNOS, Databricks

What You’ll Get

• Up to 40% off any standard Hertz Rentalin a Corporate country
• Paid Time Off
• Employee Assistance Programme for employees and family

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