Data engineer h1.location_city

Position Summary:

This is a contract/temporary position. Associate selected will be payrolled (W-2) through a staffing agency.

Develops dimensional models following medallion architecture patterns, ensures data quality, and guarantees reliable data delivery for analytics on a Snowflake platform. This involves building and maintaining dbt transformation pipelines, orchestrating with Dagster, and implementing various data ingestion methods, including Fivetran, Boomi, and custom Python pipelines for unique source systems.

Key Responsibilities:

• Design and develop dbt models following the medallion architecture (bronze > silver > gold layers).
• Build dimensional models, including fact and dimension tables, for analytics consumption.
• Implement and maintain data quality tests using dbt’s testing framework.
• Optimize SQL transformations within dbt for performance in Snowflake.
• Write and maintain schema documentation with model and column descriptions, and apply dbt best practices (refs, sources, macros, tags).
• Configure and maintain Dagster assets to orchestrate dbt model execution, including implementing schedules and sensors.
• Monitor pipeline health through the Dagster UI and resolve execution failures.
• Build and maintain bespoke Python-based ingestion pipelines for source systems without native connector support.
• Participate in code reviews for dbt models and Dagster configurations, and use tools like sqlfluff for code quality.
• Collaborate with analysts and data consumers, and document data models, transformation logic, and operational procedures.

Minimum Qualifications / Other Expectations:

• Strong SQL skills with ability to write and optimize analytical queries
  Python proficiency for building data ingestion pipelines and scripting
• Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
• Experience with dbt or willingness to learn quickly (we use dbt extensively)
• Familiarity with data warehousing concepts on Snowflake, BigQuery, or similar comfort with Git-based development workflows
• Experience with dbt in a production environment
• Familiarity with dimensional modeling (fact and dimension tables)
• Experience with layered data architectures (staging, intermediate, mart patterns)
• Exposure to workflow orchestration tools (Dagster, Airflow, Prefect)
• Understanding of data quality testing approaches

Preferred:

• 3 - 5 years in analytics engineering or data engineering