• 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

• Design and maintain scalable data architecture, pipelines, and models using dbt, Airflow, and Redshift. • Build reusable datasets that power analytics, experimentation, and business intelligence. • Collaborate with Engineering to define event tracking and instrumentation standards. • Develop core metrics, dashboards, and self-serve tools for stakeholders. • Partner with Product, Marketing, and Engineering to uncover insights on user behavior and growth. • Ensure data quality, reliability, and governance through validation and testing. • Communicate findings clearly to technical and non-technical audiences. • Continuously improve workflows, documentation, and analytics processes.

• Define and govern the product event taxonomy across services and applications • Partner with engineering teams to establish clear instrumentation contracts and naming standards • Own the modeling patterns that translate event collection pipelines into durable warehouse datasets • Ensure event data is reliable, deduplicated, and usable for analytics and modeling • Transform raw events into reusable behavioral datasets such as sessions, feature usage, funnels, retention cohorts, and customer journeys • Design models that enable product teams to analyze feature adoption, engagement, and lifecycle behavior • Maintain modeling patterns that support both exploratory analysis and production use cases • Define and maintain canonical entities such as Agency, Location, Contact, Conversation, Campaign, Spend, Usage, and Outcomes • Establish durable fact and dimension models that connect behavioral events to business entities • Ensure relationships between entities remain consistent and scalable across teams and product surfaces • Build warehouse models that power product analytics platforms • Ensure metrics in analytics tools and warehouse metrics resolve to the same definitions • Provide standardized datasets for funnels, cohorts, retention analysis, and product experimentation • Build behavioral and feature‑ready datasets used by data science for lifecycle modeling, experimentation, and prediction • Ensure datasets are stable, versioned, and reproducible for downstream ML workflows • Establish modeling patterns, dbt conventions, macros, and documentation standards used across analytics engineering • Design tenant‑safe models that support multi‑tenant workloads and high‑concurrency analytics • Partner with platform teams to ensure models are performant for both internal analytics and in‑app experiences • Define tests, freshness expectations, and invariants for behavioral datasets • Implement automated validation for event completeness and schema consistency • Partner with platform and engineering teams to detect and resolve issues before they impact analytics or customers • Establish reusable modeling patterns and best practices • Review work from analytics engineers and raise the bar for correctness, clarity, and maintainability • Help shape the long‑term architecture of the behavioral data platform

• You are responsible for stable, high-performance ELT processes on modern cloud data platforms • You develop and operate scalable data pipelines and transformations using dbt • You ensure high data quality • You design data models that directly answer business and operational questions • You work closely with business units, support operational management, and independently identify relevant deviations and trends • You advise internal stakeholders on data-related issues and translate requirements into sustainable analytics solutions • You share your knowledge within the team, establish best practices, and create technical documentation for the efficient use of data products • You work pragmatically and solution-oriented, and know how to solve challenges independently or together with the community