Design and own the curated data model layer that transforms raw ingested data into business-ready entities consumed by a Customer Data Platform (CDP) and business users. This role sits at the core of a large-scale data initiative  you'll be the person who takes 18 distinct data domains and makes them trustworthy, accessible, and activation-ready.THE ROLEWe're looking for an Analytics Engineering Lead to architect and deliver the curated layer of a modern data platform. You'll work at the intersection of data architecture, engineering rigor, and business strategy  translating complex, multi-domain data challenges into scalable, well-modeled solutions that teams actually trust and use.This is a hands-on technical leadership role. You'll report directly to the Internal Technical Architect and serve as the subject matter expert on dbt, dimensional modeling, and data quality. You'll also be a critical bridge between engineering and business stakeholders  ensuring model outputs match real activation needs.Curated Data ModelingDesign curated models across all 18 data domains — including Identity, Engagement, Demographics, Propensity, Geographic, Behavioral, Products, and Segmentation — transforming raw ingested data into clean, business-ready entitiesImplement a structured dbt project architecture following the staging → intermediate → marts pattern, with full testing and documentation at every layerBuild an enrichment layer that combines first-party data with third-party signals to produce high-value, activation-ready outputsData Quality & ObservabilityDefine and enforce a comprehensive data quality framework covering freshness checks, volume anomaly detection, and schema drift alertingEstablish testing standards and documentation practices that make the data layer auditable and trustworthy for both technical and business consumersStakeholder EngagementInterface directly with business stakeholders to validate that model outputs align with CDP activation needs and downstream consumption requirementsTranslate business requirements into data model specifications, and translate model constraints back into business-friendly languageStandards & LeadershipSet analytics engineering best practices across the project — naming conventions, modular design patterns, incremental model strategies, and code review standardsPartner with data engineers, CDP architects, and analysts to ensure seamless data flow from ingestion through activation
