About the role
The Data Engineer 2 will join a fun, dynamic team to help solve integration and data problems relating to sports and entertainment. This role will design, build, and maintain data pipelines using a modern data stack — combining data from cloud and on-premises systems, transforming it with dbt, and orchestrating workflows end to end. The position is responsible for ETL/ELT from many disparate systems into the data warehouse and offers the opportunity to own projects from beginning to end.
The team is finalizing its target data architecture between two directions — a balanced on-prem/cloud modernization and a best-in-class cloud-native platform — so the right candidate will bring strong fundamentals that apply to both, along with depth in at least one. Architectural guidance will be available, but this engineer must be able to implement data models effectively and independently. The right candidate will be motivated to learn, contribute to the team and organization, and grow along with the business.
What you will do:
Data Integration & Pipeline Development
- Design,
build, and maintain automated data ingestion pipelines from cloud and on-premises sources into Snowflake using Azure Data Factory (ADF).
- Translate business requirements into efficient ELT workflows using ADF for orchestration/ingestion and dbt (Core or Cloud) for in-warehouse transformations.
- Build, test, and document scalable data models and transformations in dbt layered directly on Snowflake.
- Monitor, schedule, and optimize ADF pipelines, dataset triggers, and dbt runs to ensure high pipeline reliability and data freshness.
- Implement dimensional data models (Star/Snowflake schemas) optimized for Snowflake performance and cost efficiency.
- Perform data cleansing, standardization, and staging using ADLS Gen2/Blob storage landing zones.
- Write Python code (or Azure Functions/Snowpark routines) to handle complex API extractions, custom transformations, and automated utility tasks.