Data Engineer

HMP Global is a healthcare event and education company. The Data Engineer builds and tests dbt models and Snowflake marts for BI and reporting, working with BI analysts and implementing designs from the Data Architect.

Responsibilities:

• Design, build and maintain dbt models in Snowflake across a medallion architecture (bronze, silver, gold), turning raw data into trusted, analysis-ready data products
• Build star schema marts with fact and dimension tables, and deliver them directly to the BI team for consumption
• Meet with BI analysts to understand reporting needs, agree model definitions and grain, and ship marts that answer their questions
• Work with the Data Architect to understand each design before you build, raise gaps early, and implement it as specified
• Write dbt tests and unit tests for every model: not null, unique, relationships, accepted values, referential integrity and business logic tests with expected outputs
• Document every model, column and business rule in dbt, with lineage, so BI and engineers can use it without asking you
• Review teammates' dbt code and follow shared standards for naming, layering and materializations
• Tune Snowflake SQL and model materializations to keep queries fast and credit usage low
• Know how data reaches Snowflake through ELT pipelines (Rivery today), and when needed run them, troubleshoot failures and set up new ones
• Use AI tools responsibly to work faster and research more effectively, such as drafting SQL, dbt tests and documentation, while reviewing and validating all output and keeping sensitive data protected

Requirements:

• Applicants must be legally authorized to work in the United States on a permanent basis without the need for current or future employer sponsorship
• 3 to 5 years in data engineering or analytics engineering, building and owning dbt models in production on Snowflake
• Expert knowledge on dbt platform
• Expert Snowflake advanced SQL
• Deep knowledge of dimensional modeling (star schemas, facts, dimensions, grain, slowly changing dimensions) and hands-on medallion (bronze, silver, gold) layering
• Working knowledge of ELT pipelines and an ingestion or orchestration tool such as Rivery, Fivetran or Airflow, enough to run, troubleshoot and set up pipelines when needed
• A habit of unit testing and data quality testing, with tests written alongside each model
• Strong technical documentation: clear model descriptions, lineage and business rules
• Uses AI tools responsibly to improve speed and research, with sound judgment on checking output and protecting sensitive data
• Git, pull request reviews and CI/CD for dbt; bachelor's degree in a quantitative or technical field, or equivalent experience
• Snowflake Cortex experience, to build a semantic layer
• Familiarity with Power BI semantic models, especially building models that perform well in reports
• SnowPro Core or dbt Analytics Engineering certification
• Healthcare, media or marketing data, such as HCP, event or engagement data