Job Description & Details
This is a heavy-hitting data engineering role where you will own the end-to-end lifecycle of production cloud pipelines using Snowflake and AWS. If you enjoy building scalable, medallion-architecture pipelines and wrestling with complex data transformations rather than just pushing tickets, this gig is worth a serious look.
What You'll Actually Be Doing
Your day-to-day will revolve around designing and building ingestion and transformation pipelines that feed the Bronze, Silver, and Gold layers in Snowflake. You'll write modular dbt models, optimize heavy-duty SQL, and orchestrate everything using Airflow. Expect to spend a solid chunk of your time setting up automated data quality tests with frameworks like SODA, bridging AWS integrations via APIs and events, and keeping production systems humming without constant firefighting.
The Core Tech Stack
You need absolute fluency in Snowflake, AWS data engineering services, dbt, and Apache Airflow. Snowflake performance tuning and advanced SQL are non-negotiable here, as is your ability to spin up robust pipelines using Kafka, REST APIs, and Git-based CI/CD workflows. They want someone who doesn't just write code, but owns the observability and testing layers to ensure data is clean before it ever hits production.
Interview Expectations
Expect the hiring team to grill you on how you handle massive data drift and schema changes in a production medallion architecture, so be ready to walk through a real-world scenario where your dbt models or Airflow DAGs broke and how you debugged it. They'll also test your Snowflake optimization chops—be prepared to discuss query profile analysis, clustering keys, and secure data sharing strategies under pressure.
Application Advice
To get past the ATS and grab the recruiter's attention, make sure your resume explicitly highlights your hands-on production experience with Snowflake, dbt, and Airflow orchestration. Don't just list technologies; write punchy bullet points detailing the scale of the pipelines you've built and the automated data quality frameworks like SODA or Great Expectations that you've implemented.