Develop and maintain dbt models that transform event and customer data into reusable dimensions and facts
Implement consistent transformations for identifiers, tenancy, deduplication, and time-based logic
Implement and maintain dbt tests for schema, freshness, and business logic validation
Investigate and remediate issues surfaced by tests, monitoring, or product stakeholders
Ensure models, columns, and metrics are well documented and aligned with standards
Maintain tags, ownership metadata, and lineage annotations where applicable
Build and maintain tables used by dashboards, segmentation, Ads Manager reporting, and embedded analytics
Refactor ad hoc or feature-specific logic into standardized reusable assets
Coordinate with Product Data Engineering and the CDP team on schema changes, backfills, and event data issues
Partner with product engineers to validate outputs and support safe consumption patterns

4+ years of experience in analytics engineering or data engineering roles
Strong hands-on experience with dbt and SQL, and comfort operating in production
Proven experience building and maintaining reusable models with tests and documentation
Familiarity with event-based/product data and multi-tenant modeling patterns
Experience building production models for both warehouse analytics and low-latency analytics workloads

Senior Analytics Engineer – Product, Customer Analytics

Key skills