Data Engineer

Details

• Design, implement, and optimize data pipelines that extract, transform, and load data into Snowflake from multiple sources using Airflow and AWS services
• Build modular, well-documented dbt models with strong test coverage to serve business reporting, lifecycle marketing, and experimentation use cases
• Partner with analytics and business stakeholders to define source-to-target transformations and implement them in dbt
• Maintain and improve our orchestration layer (Airflow/Astronomer) to ensure reliability, visibility, and efficient dependency management
• Collaborate on data model design best practices, including dimensional modeling, naming conventions, and versioning strategies
• dbt: Hands-on experience developing dbt models at scale, including use of macros, snapshots, testing frameworks, and documentation. Familiarity with dbt Cloud or CLI workflows
• Snowflake: Strong SQL skills and understanding of Snowflake architecture, including query performance tuning, cost optimization, and use of semi-structured data
• Airflow: Solid experience managing Airflow DAGs, scheduling jobs, and implementing retry logic and failure handling; familiarity with Astronomer is a plus
• Data Modeling: Proficient in dimensional modeling and building reusable data marts that support analytics and operational use cases
• Experience with Oracle
• Familiarity with AWS services such as DMS, Kinesis, and Firehose for ingesting and transforming data
• Segment: Familiarity with event data and related flows, piping data in and out of Segment