Job Description As a Marketing Data Engineer, you’ll be responsible for delivering reporting and insight to inform strategy and commercial decision making. Taking information from multiple sources including Advertising Platforms and Data Warehouses to provide insight that helps elevate the understanding of customers, potential customers, advertising, commercial ROI and website. Building reporting that tells a story, creating visualised reporting that drive actionable insights.' A super opportunity to join a fast growing travel business, this role is a newly created role, providing great scope to grow within the business. It requires a naturally curious, inquisitive mindset, someone who loves to delve into the data, solve problems, stitch things together and help the teams gain greater insight into the customer and commercial detail. You’ll be data hungry, love learning and finding work arounds and fixing gaps – a role where you can add great value and be involved across the business: Pipeline Development: Design, build, and maintain scalable ETL/ELT pipelines to ingest data from diverse sources including booking engines, and digital marketing platforms like Google Ads or Meta Ads. And develop a plan for leveraging customer data in the absence of CRM & CDP systems / a plan for the business investing in the implementation of such a system. Marketing Analytics Support: Develop data models to support advanced marketing use cases such as Marketing Mix Modelling (MMM), multi-touch attribution, and customer lifetime value (CLV) predictions. Data Quality & Governance : Ensure all data handling complies with UK GDPR and PECR, specifically managing PII (Personally Identifiable Information) and marketing consent across all villa-related datasets. Stakeholder Collaboration: Translate complex technical data structures into clear insights for non-technical marketing managers to help them understand campaign performance and villa occupancy trends. Infrastructure Management: Manage and optimise cloud-based data warehouses or lakes, typically using Microsoft Azure, AWS, or Google Cloud Platform (GCP). Technical Proficiency: Expert-level SQL and Python are essential for data transformation and automation. Tooling Experience: Familiarity with modern data stack tools such as dbt for transformations, Airflow for orchestration, and Terraform for infrastructure-as-code.