Scale.jobs is seeking an Analytics Engineer / dbt Developer to own the transformation layer for analytics teams across client organizations. The role involves building, testing, and documenting dbt models, ensuring data quality, and collaborating with data scientists and analysts to meet their data consumption needs.
Responsibilities:
- Design and build dbt models following dimensional modeling principles (star schema) and One Big Table patterns depending on use case requirements
- Write comprehensive dbt tests: schema tests, singular tests, and custom macros to enforce data quality as a first-class engineering concern
- Administer Snowflake or BigQuery environments: warehouse sizing, cost optimization, access controls, and query performance tuning
- Build and maintain Airflow or Prefect DAGs to orchestrate dbt runs alongside upstream data ingestion jobs
- Document data lineage, metric definitions, and business logic in dbt docs and internal wikis
- Collaborate with data scientists and analysts to understand their consumption requirements and ensure data models serve them well
- Evaluate and integrate Fivetran, Airbyte, or custom connectors as new data sources are onboarded
Requirements:
- 2–5 years of analytics engineering or data engineering experience - with hands-on dbt development in a production environment
- Advanced SQL: you write complex analytical queries fluently in Snowflake or BigQuery SQL dialect
- Snowflake or BigQuery administration: you understand how to make it performant and cost-efficient, not just make it work
- dbt: model development, testing, documentation, macros, and packages - all of it, not just the basics
- Data modeling fundamentals: dimensional modeling, OBT patterns, slowly changing dimensions
- Python basics for Airflow/Prefect orchestration and utility scripts
- Looker LookML
- Metabase
- dbt Semantic Layer
- experience with Fivetran connector management