The mission Build a data platform that works as a business asset, not a cost centre. Feature store and lakehouse on Databricks, with point-in-time correctness, freshness guarantees and full lineage. Data quality by default. Freshness, completeness and drift are monitored, and engineers are held to the standards you set. Analytics that move money. Portfolio performance and unit economics become decision-ready numbers, together with Credit Risk and Data Product. Agent-first pipelines. Agents are production components, with guardrails, evals and observability, and you control their cost and latency. Governance as a speed advantage. GDPR-aware by design, audit-ready, and fast to ship. This is a business function. Every pipeline and every metric exists to create or protect monetary value. You will plan budget, tooling spend and headcount accordingly. What you'll own: The Data Engineering & Analytics team: hiring, coaching and performance, with Staff Engineers as your architectural anchors. Pipeline and feature-store architecture direction for every data product that credit and decisioning depend on. The platform roadmap, co-authored with the Director of Data & AI and the Data Product Lead. The AI-first engineering standard across the team.