Role overview
As Data Engineer Manager, you will own the data platform that powers our AI programmes, analytics and reporting. Reporting to the Solutions & Platform Lead and based in Madrid, you will lead the Foundations build: defining the architecture, engineering standards, roadmap and delivery team that brings the platform to life.
This is a hands-on leadership role with a strong technical core. You will guide the engineering side of the data quality programme, shape the Azure-based platform architecture, and work closely with AI, business and technology stakeholders to deliver trusted data products that support guest experience, commercial performance and operational excellence.

Main responsibilities
• Lead the engineering workstream for data quality, including issue discovery, sizing, root-cause analysis, historical correction and weekly KPI monitoring.
• Own and evolve the target data architecture on Azure, covering ingestion, datalake, data warehouse and serving layers for AI use cases.
• Turn the Foundations programme into a clear roadmap and backlog,



delivering in increments that unlock value early.
• Build and lead a high-performing data engineering team, including hiring, coaching and performance management.
• Manage external partners and vendors through clear scope, acceptance criteria and delivery governance.
• Define and enforce engineering standards across coding, testing, CI/CD, orchestration, naming, documentation and code review.
• Embed data quality, validation, referential integrity, matching, observability, lineage and SLA controls into the platform.
• Ensure security, access control, guest data protection and FinOps principles are applied by design.
• Partner with AI Project Managers, AI Engineers and Data Scientists to translate data needs into realistic delivery commitments.
• Prepare clear reporting on roadmap progress, data quality KPIs, incidents, risks, SLAs and cloud spend for senior stakeholders.

What success looks like in the first

📌 Data Engineer Manager (Madrid)
🏢 Sani/Ikos Group
📍 Madrid