Analytics Engineer / dbt Developer

About The RoleThe role owns the transformation layer - building, testing, and documenting the dbt models that analytics teams across client organizations use daily.This is a role where data modeling craft, SQL depth, and attention to data quality matter more than any single tool certification.Key ResponsibilitiesDesign and build dbt models following dimensional modeling principles (star schema) and One Big Table patterns depending on use case requirementsWrite comprehensive dbt tests: schema tests, singular tests, and custom macros to enforce data quality as a first-class engineering concernAdminister Snowflake or BigQuery environments: warehouse sizing, cost optimization, access controls, and query performance tuningBuild and maintain Airflow or Prefect DAGs to orchestrate dbt runs alongside upstream data ingestion jobsDocument data lineage, metric definitions, and business logic in dbt docs and internal wikisCollaborate with data scientists and analysts to understand their consumption requirements and ensure data models serve them wellEvaluate and integrate Fivetran, Airbyte, or custom connectors as new data sources are onboardedWhat We Are Looking For2–5 years of analytics engineering or data engineering experience - with hands-on dbt development in a production environment (required)Advanced SQL: you write complex analytical queries fluently in Snowflake or BigQuery SQL dialectSnowflake or BigQuery administration: you understand how to make it performant and cost-efficient, not just make it workdbt: model development, testing, documentation, macros, and packages - all of it, not just the basicsData modeling fundamentals: dimensional modeling, OBT patterns, slowly changing dimensionsPython basics for Airflow/Prefect orchestration and utility scriptsBonus: Looker LookML, Metabase, dbt Semantic Layer, or experience with Fivetran connector managementLocationRemote-first (US time zones) San Francisco New York City Chicago Seattle Austin