Staff Analytics Engineer – Customer Data Platform

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