Analytics Engineering Lead
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 ROLE
We'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 Modeling
- Design 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 entities
- Implement a structured dbt project architecture following the staging intermediate marts pattern, with full testing and documentation at every layer
- Build an enrichment layer that combines first-party data with third-party signals to produce high-value, activation-ready outputs
Data Quality & Observability
- Define and enforce a comprehensive data quality framework covering freshness checks, volume anomaly detection, and schema drift alerting
- Establish testing standards and documentation practices that make the data layer auditable and trustworthy for both technical and business consumers
Stakeholder Engagement
- Interface directly with business stakeholders to validate that model outputs align with CDP activation needs and downstream consumption requirements
- Translate business requirements into data model specifications, and translate model constraints back into business-friendly language
Standards & Leadership
- Set analytics engineering best practices across the project naming conventions, modular design patterns, incremental model strategies, and code review standards
- Partner with data engineers, CDP architects, and analysts to ensure seamless data flow from ingestion through activation
