Analytics Engineer

Crexi•Los Angeles, CA

4h•Remote

About The Position

The Analytics Engineer owns the data models, KPI definitions, and trusted datasets that power decisions across the company. This role sits on the Internal Data Products team and is responsible for translating business logic into well-tested, version-controlled dbt models in Snowflake. You will work closely with Product, Sales, Finance, and Marketing to define what we measure, how we measure it, and ensure the data is reliable enough for stakeholders to act on without asking questions.

Requirements

Bachelor’s degree in an analytical field (e.g. Economics, Finance, Mathematics, Statistics, Computer Science).
2–5 years of experience in analytics engineering, data engineering, or a data analyst role that included dbt and data modeling.
Strong SQL skills with experience writing complex queries in Snowflake or a comparable cloud data warehouse; hands-on experience with dbt (Core or Cloud), including writing models, tests, and documentation.
Experience with a BI tool such as Sigma, Looker, or Tableau, and familiarity with product analytics platforms (Mixpanel, Amplitude) and event-based data.

Responsibilities

Defines and instruments company-level and product-level KPIs in partnership with Finance, Product, and Sales.
Owns and develops the core dbt models: builds, tests, and documents staging, intermediate, and mart models that serve as the single source of truth for business metrics.
Builds and maintains Sigma dashboards and self-service datasets that stakeholders use without analyst intervention.
Designs and manages ETL and Reverse ETL syncs (Fivetran) that push curated data into Salesforce, HubSpot, and Iterable.
Partners with Product and Engineering to instrument event tracking (Mixpanel), define behavioral metrics, and run multivariate tests that inform product decisions.
Contributes to entity resolution efforts: helps build canonical account and contact models in Snowflake that downstream systems reference.
Works cross-functionally with Sales, Marketing, Finance, and Customer Success to translate business questions into modeled, governed data.
Proactively identifies data quality issues, builds dbt tests and alerts, and improves pipeline reliability.
Builds and implements AI-powered workflows for internal data products, including automated reporting agents, LLM-driven data validation, and natural-language interfaces to warehouse data.
Ensures compliance with data privacy and security regulations and maintains ethical data handling practices.
Identifies opportunities to improve data-driven decision-making and proposes solutions